Awesome-Continual-Learning

This is the paper list of our paper ''A Comprehensive Survey of Continual Learning: Theory, Method and Application'' [link]. We will continue to add useful resources to this repo.

Survey

• [2024 TPAMI] A Comprehensive Survey of Continual Learning: Theory, Method and Application [paper]
• [2023 arXiv] A Survey on Incremental Update for Neural Recommender Systems [paper]
• [2023 arXiv] Deep Class-Incremental Learning: A Survey [paper]
• [2023 arXiv] Towards Label-Efficient Incremental Learning A Survey [paper]
• [2022 arXiv] Continual Learning of Natural Language Processing Tasks A Survey [paper]
• [2022 Trends in Neurosciences] Contributions by metaplasticity to solving the Catastrophic Forgetting Problem [paper]
• [2022 TPAMI] Class-incremental learning survey and performance evaluation on image classification [paper]
• [2022 NMI] Biological underpinnings for lifelong learning machines [paper]
• [2022 Neurocomputing] Online Continual Learning in Image Classification: An Empirical Survey [paper]
• [2022 JAIR] Towards Continual Reinforcement Learning [paper]
• [2021 arXiv] Recent Advances of Continual Learning in Computer Vision: An Overview [paper]
• [2021 TPAMI] A continual learning survey: Defying forgetting in classification tasks [paper]
• [2021 Neural Computation] Replay in Deep Learning: Current Approaches and Missing Biological Elements [paper]
• [2020 Trends in Cognitive Sciences] Embracing Change: Continual Learning in Deep Neural Networks [paper]
• [2020 TPAMI] Class-incremental learning survey and performance evaluation on image classification [paper]
• [2020 COLING] Continual Lifelong Learning in Natural Language Processing: A Survey [paper]
• [2019 Neural Networks] Continual Lifelong Learning with Neural Networks: A Review [paper]

Papers

2023

• [2023 CVPR] Dealing With Cross-Task Class Discrimination in Online Continual Learning [paper][code]
• [2023 CVPR] Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning [paper][code]
• [2023 CVPR] GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-shot Class Incremental Task [paper]
• [2023 CVPR] EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization [paper]
• [2023 CVPR] Endpoints Weight Fusion for Class Incremental Semantic Segmentation [paper]
• [2023 CVPR] On the Stability-Plasticity Dilemma of Class-Incremental Learning [paper]
• [2023 CVPR] Regularizing Second-Order Influences for Continual Learning [paper][code]
• [2023 CVPR] Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning [paper]
• [2023 CVPR] Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning [paper]
• [2023 CVPR] A Probabilistic Framework for Lifelong Test-Time Adaptation [paper][code]
• [2023 CVPR] Continual Semantic Segmentation with Automatic Memory Sample Selection [paper]
• [2023 CVPR] Exploring Data Geometry for Continual Learning [paper]
• [2023 CVPR] PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning [paper][code]
• [2023 CVPR] Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning [paper][code]
• [2023 CVPR] Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation [paper]
• [2023 CVPR] Continual Detection Transformer for Incremental Object Detection [paper][code]
• [2023 CVPR] PIVOT: Prompting for Video Continual Learning [paper]
• [2023 CVPR] CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning [paper][code]
• [2023 CVPR] Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions [paper]
• [2023 CVPR] Class-Incremental Exemplar Compression for Class-Incremental Learning [paper][code]
• [2023 CVPR] Dense Network Expansion for Class Incremental Learning [paper]
• [2023 ICLR] Online Bias Correction for Task-Free Continual Learning [paper]
• [2023 ICLR] Sparse Distributed Memory is a Continual Learner [paper][code]
• [2023 ICLR] Continual Learning of Language Models [paper]
• [2023 ICLR] Progressive Prompts: Continual Learning for Language Models without Forgetting [paper][code]
• [2023 ICLR] Is Forgetting Less a Good Inductive Bias for Forward Transfer? [paper]
• [2023 ICLR] Online Boundary-Free Continual Learning by Scheduled Data Prior [paper]
• [2023 ICLR] Incremental Learning of Structured Memory via Closed-Loop Transcription [paper][code]
• [2023 ICLR] Better Generative Replay for Continual Federated Learning [paper]
• [2023 ICLR] 3EF: Class-Incremental Learning via Efficient Energy-Based Expansion and Fusion [paper]
• [2023 ICLR] Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning [paper]
• [2023 ICLR] Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting [paper]
• [2023 ICLR] Building a Subspace of Policies for Scalable Continual Learning [paper][code]
• [2023 ICLR] A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning [paper][code1/code2]
• [2023 ICLR] Continual evaluation for lifelong learning: Identifying the stability gap [paper][code]
• [2023 ICLR] Continual Unsupervised Disentangling of Self-Organizing Representations [paper]
• [2023 ICLR] Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning [paper]
• [2023 ICLR] Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning [paper][code]
• [2023 ICLR] On the Soft-Subnetwork for Few-Shot Class Incremental Learning [paper][code]
• [2023 ICLR] Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning [paper][code]
• [2023 ICLR] Task-Aware Information Routing from Common Representation Space in Lifelong Learning [paper][code][code]

2022

• [2022 WACV] Online Continual Learning Via Candidates Voting [paper]
• [2022 WACV] Knowledge Capture and Replay for Continual Learning [paper]
• [2022 WACV] FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning [paper][code]
• [2022 WACV] Dataset Knowledge Transfer for Class-Incremental Learning without Memory [paper][code]
• [2022 TPAMI] Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation [paper][code]
• [2022 TPAMI] MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning [paper]
• [2022 TPAMI] Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks [paper][code]
• [2022 TPAMI] Class-Incremental Continual Learning into the eXtended DER-verse [paper][code]
• [2022 TNNLS] Self-Training for Class-Incremental Semantic Segmentation [paper]
• [2022 PRL] Continual Semi-Supervised Learning through Contrastive Interpolation Consistency [paper][code]
• [2022 NeurIPS] Task-Free Continual Learning via Online Discrepancy Distance Learning [paper]
• [2022 NeurIPS] SparCL Sparse Continual Learning on the Edge [paper][code]
• [2022 NeurIPS] S-Prompts Learning with Pre-trained Transformers An Occam’s Razor for Domain Incremental Learning [paper][code1/code2]
• [2022 NeurIPS] Retrospective Adversarial Replay for Continual Learning [paper]
• [2022 NeurIPS] Repeated Augmented Rehearsal: A Simple but Strong Baseline for Online Continual Learning [paper][code]
• [2022 NeurIPS] On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning [paper][code]
• [2022 NeurIPS] On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting [paper][code]
• [2022 NeurIPS] Memory Efficient Continual Learning with Transformers [paper]
• [2022 NeurIPS] Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation [paper][code]
• [2022 NeurIPS] Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting [paper]
• [2022 NeurIPS] How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning [paper][code]
• [2022 NeurIPS] Few-Shot Continual Active Learning by a Robot [paper]
• [2022 NeurIPS] Exploring Example Influence in Continual Learning [paper][code]
• [2022 NeurIPS] Disentangling Transfer in Continual Reinforcement Learning [paper]
• [2022 NeurIPS] Continual Learning In Environments With Polynomial Mixing Times [paper][code]
• [2022 NeurIPS] Continual learning a feature extraction formalization, an efficient algorithm, and fundamental obstructions [paper]
• [2022 NeurIPS] CLiMB A Continual Learning Benchmark for Vision-and-Language Tasks [paper][code]
• [2022 NeurIPS] CGLB Benchmark Tasks for Continual Graph Learning [paper]
• [2022 NeurIPS] Beyond Not-Forgetting Continual Learning with Backward Knowledge Transfer [paper]
• [2022 NeurIPS] ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation [paper]
• [2022 NeurIPS] A Theoretical Study on Solving Continual Learning [paper][code]
• [2022 NeurIPSW] A Simple Baseline that Questions the Use of Pretrained-Models in Continual Learning [paper][code]
• [2022 Neural Networks] Efficient Perturbation Inference and Expandable Network for Continual Learning [paper]
• [2022 NAACL] Overcoming Catastrophic Forgetting During Domain Adaptation of Seq2seq Language Generation [paper]
• [2022 MM] Semantics-Driven Generative Replay for Few-Shot Class Incremental Learning [paper]
• [2022 MM] Incremental Few-Shot Semantic Segmentation via Embedding Adaptive-Update and Hyper-class Representation [paper]
• [2022 MM] Class Gradient Projection For Continual Learning [paper]
• [2022 IJCAI] Learning from Students: Online Contrastive Distillation Network for General Continual Learning [paper]
• [2022 IJCAI] DyGRAIN: An Incremental Learning Framework for Dynamic Graphs [paper]
• [2022 IJCAI] Continual Semantic Segmentation Leveraging Image-level Labels and Rehearsal [paper]
• [2022 IJCAI] Continual Federated Learning Based on Knowledge Distillation [paper]
• [2022 IJCAI] CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation [paper][code]
• [2022 ICPR] Effects of Auxiliary Knowledge on Continual Learning [paper][code]
• [2022 ICML] Wide Neural Networks Forget Less Catastrophically [paper]
• [2022 ICML] VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty [paper]
• [2022 ICML] Proving Theorems using Incremental Learning and Hindsight Experience Replay [paper]
• [2022 ICML] Online Continual Learning through Mutual Information Maximization [paper]
• [2022 ICML] NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks [paper][code]
• [2022 ICML] Improving Task-free Continual Learning by Distributionally Robust Memory Evolution [paper][code]
• [2022 ICML] Forget-free Continual Learning with Winning Subnetworks [paper]
• [2022 ICML] Continual Learning with Guarantees via Weight Interval Constraints [paper][code]
• [2022 ICML] Continual Learning via Sequential Function-Space Variational Inference [paper]
• [2022 ICLR] TRGP: Trust Region Gradient Projection for Continual Learning [paper][code]
• [2022 ICLR] Towards Continual Knowledge Learning of Language Models [paper][code]
• [2022 ICLR] Subspace Regularizers for Few-Shot Class Incremental Learning [paper][code]
• [2022 ICLR] Representational Continuity for Unsupervised Continual Learning [paper][code1/code2]
• [2022 ICLR] Pretrained Language Model in Continual Learning: A Comparative Study [paper]
• [2022 ICLR] Online Coreset Selection for Rehearsal-based Continual Learning [paper]
• [2022 ICLR] Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference [paper][code]
• [2022 ICLR] New Insights on Reducing Abrupt Representation Change in Online Continual Learning [paper][code1/code2]
• [2022 ICLR] Model Zoo: A Growing “Brain” That Learns Continually [paper][code]
• [2022 ICLR] Memory Replay with Data Compression for Continual Learning [paper][code]
• [2022 ICLR] Looking Back on Learned Experiences For Class/task Incremental Learning [paper]
• [2022 ICLR] LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 [paper][code]
• [2022 ICLR] Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System [paper][code]
• [2022 ICLR] Learning Curves for Continual Learning in Neural Networks: Self-Knowledge Transfer and Forgetting [paper]
• [2022 ICLR] Information-theoretic Online Memory Selection for Continual Learning [paper]
• [2022 ICLR] How Well Does Self-Supervised Pre-Training Perform with Streaming Data? [paper]
• [2022 ICLR] Effect of Scale on Catastrophic Forgetting in Neural Networks [paper]
• [2022 ICLR] Continual Normalization: Rethinking Batch Normalization for Online Continual Learning [paper][code]
• [2022 ICLR] Continual Learning with Recursive Gradient Optimization [paper]
• [2022 ICLR] Continual Learning with Filter Atom Swapping [paper][code]
• [2022 ICLR] CoMPS: Continual Meta Policy Search [paper]
• [2022 ICLR] CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability [paper][code]
• [2022 EMNLP] Continual Training of Language Models for Few-Shot Learning [paper][code]
• [2022 ECCV] Transfer without Forgetting [paper][code]
• [2022 ECCV] The Challenges of Continuous Self-Supervised Learning [paper]
• [2022 ECCV] S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning [paper][code]
• [2022 ECCV] R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning [paper][code]
• [2022 ECCV] Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation [paper][code]
• [2022 ECCV] Online Task-free Continual Learning with Dynamic Sparse Distributed Memory [paper]
• [2022 ECCV] Online Continual Learning with Contrastive Vision Transformer [paper]
• [2022 ECCV] Novel Class Discovery without Forgetting [paper]
• [2022 ECCV] Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions [paper]
• [2022 ECCV] Long-Tailed Class Incremental Learning [paper][code]
• [2022 ECCV] Learning with Recoverable Forgetting [paper]
• [2022 ECCV] Incremental Task Learning with Incremental Rank Updates [paper]
• [2022 ECCV] incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection [paper]
• [2022 ECCV] Helpful or Harmful Inter-Task Association in Continual Learning [paper]
• [2022 ECCV] Generative Negative Text Replay for Continual Vision-Language Pretraining [paper]
• [2022 ECCV] FOSTER: Feature Boosting and Compression for Class-Incremental Learning [paper][code]
• [2022 ECCV] Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay [paper][code]
• [2022 ECCV] Few-Shot Class-Incremental Learning from an Open-Set Perspective [paper][code]
• [2022 ECCV] DualPrompt Complementary Prompting for Rehearsal-free Continual Learning [paper][code]
• [2022 ECCV] DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning [paper]
• [2022 ECCV] CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One [paper][code]
• [2022 ECCV] Class-incremental Novel Class Discovery [paper][code]
• [2022 ECCV] Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer [paper][code]
• [2022 ECCV] Balancing Stability and Plasticity through Advanced Null Space in Continual Learning [paper]
• [2022 ECCV] Balancing between Forgetting and Acquisition in Incremental Subpopulation Learning [paper]
• [2022 ECCV] Anti-Retroactive Interference for Lifelong Learning [paper][code]
• [2022 CVPR] vCLIMB: A Novel Video Class Incremental Learning Benchmark [paper]
• [2022 CVPR] Towards Better Plasticity-Stability Trade-off in Incremental Learning A Simple Linear Connector [paper]
• [2022 CVPR] Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning [paper]
• [2022 CVPR] Self-Supervised Models are Continual Learners [paper][code]
• [2022 CVPR] Representation Compensation Networks for Continual Semantic Segmentation [paper][code]
• [2022 CVPR] Probing Representation Forgetting in Supervised and Unsupervised Continual Learning [paper]
• [2022 CVPR] Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation [paper][code]
• [2022 CVPR] Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries [paper][code]
• [2022 CVPR] On Generalizing Beyond Domains in Cross-Domain Continual Learning [paper]
• [2022 CVPR] Not Just Selection, but Exploration Online Class-Incremental Continual Learning via Dual View Consistency [paper][code]
• [2022 CVPR] Mimicking the Oracle An Initial Phase Decorrelation Approach for Class Incremental Learning [paper][code]
• [2022 CVPR] MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning [paper]
• [2022 CVPR] Meta-attention for ViT-backed Continual Learning [paper][code]
• [2022 CVPR] Lifelong Graph Learning [paper][code1/code2]
• [2022 CVPR] Learning to Prompt for Continual Learning [paper][code]
• [2022 CVPR] Learning to Imagine Diversify Memory for Incremental Learning using Unlabeled Data [paper][code]
• [2022 CVPR] Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning [paper]
• [2022 CVPR] Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding [paper][code]
• [2022 CVPR] Incremental Learning in Semantic Segmentation from Image Labels [paper][code]
• [2022 CVPR] General Incremental Learning with Domain-aware Categorical Representations [paper]
• [2022 CVPR] GCR: Gradient Coreset Based Replay Buffer Selection For Continual Learning [paper]
• [2022 CVPR] Forward Compatible Few-Shot Class-Incremental Learning [paper][code]
• [2022 CVPR] Few-Shot Incremental Learning for Label-to-Image Translation [paper]
• [2022 CVPR] Federated Class-Incremental Learning [paper][code]
• [2022 CVPR] Energy-based Latent Aligner for Incremental Learning [paper][code]
• [2022 CVPR] DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion [paper][code]
• [2022 CVPR] Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches [paper]
• [2022 CVPR] Continual Learning with Lifelong Vision Transformer [paper]
• [2022 CVPR] Continual Learning for Visual Search with Backward Consistent Feature Embedding [paper][code]
• [2022 CVPR] Constrained Few-shot Class-incremental Learning [paper][code]
• [2022 CVPR] Class-Incremental Learning with Strong Pre-trained Models [paper][code]
• [2022 CVPR] Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation [paper][code]
• [2022 CVPR] Bring Evanescent Representations to Life in Lifelong Class Incremental Learning [paper]
• [2022 CVIU] Balanced softmax cross-entropy for incremental learning with and without memory [paper]
• [2022 COLING] Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection [paper][code]
• [2022 COLING] Dynamic Dialogue Policy for Continual Reinforcement Learning [paper]
• [2022 COLING] Continual Few-shot Intent Detection [paper]
• [2022 ACL] Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation [paper][code]
• [2022 ACL] Few-Shot Class-Incremental Learning for Named Entity Recognition [paper]
• [2022 ACL] Continual Sequence Generation with Adaptive Compositional Modules [paper][code]
• [2022 ACL] Continual Prompt Tuning for Dialog State Tracking [paper][code]
• [2022 ACL] Continual Pre-training of Language Models for Math Problem Understanding with Syntax-Aware Memory Network [paper][code]
• [2022 ACL] Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation [paper][code]
• [2022 ACL] ConTinTin: Continual Learning from Task Instructions [paper]
• [2022 AAAI] Static-Dynamic Co-teaching for Class-Incremental 3D Object Detection [paper]
• [2022 AAAI] Same State, Different Task: Continual Reinforcement Learning without Interference [paper][code]
• [2022 AAAI] Learngene: From Open-World to Your Learning Task [paper][code]
• [2022 AAAI] Continual Learning through Retrieval and Imagination [paper]
• [2022 AAAI] Adaptive Orthogonal Projection for Batch and Online Continual Learning [paper]

2021

• [2021 arXiv] SPeCiaL: Self-Supervised Pretraining for Continual Learning [paper]
• [2021 arXiv] An Empirical Investigation of the Role of Pre-training in Lifelong Learning [paper][code]
• [2021 WACV] Do not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting [paper]
• [2021 TPAMI] Incremental Object Detection via Meta-Learning [paper][code]
• [2021 TNNLS] Triple-Memory Networks: A Brain-Inspired Method for Continual Learning [paper]
• [2021 PRL] ACAE-REMIND for Online Continual Learning with Compressed Feature Replay [paper]
• [2021 NeurIPS] SSUL Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning [paper][code]
• [2021 NeurIPS] RMM: Reinforced Memory Management for Class-Incremental Learning [paper][code]
• [2021 NeurIPS] Posterior Meta-Replay for Continual Learning [paper][code]
• [2021 NeurIPS] Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima [paper][code]
• [2021 NeurIPS] Optimizing Reusable Knowledge for Continual Learning via Metalearning [paper][code]
• [2021 NeurIPS] Natural continual learning success is a journey, not (just) a destination [paper][code]
• [2021 NeurIPS] Mitigating Forgetting in Online Continual Learning with Neuron Calibration [paper]
• [2021 NeurIPS] Lifelong Domain Adaptation via Consolidated Internal Distribution [paper]
• [2021 NeurIPS] Learning where to learn: Gradient sparsity in meta and continual learning [paper][code]
• [2021 NeurIPS] Gradient-based Editing of Memory Examples for Online Task-free Continual Learning [paper
• [2021 NeurIPS] Generative vs Discriminative: Rethinking The Meta-Continual Learning [paper][code]
• [2021 NeurIPS] Formalizing the Generalization-Forgetting Trade-Off in Continual Learning [paper]
• [2021 NeurIPS] Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning [paper][code]
• [2021 NeurIPS] DualNet: Continual Learning, Fast and Slow [paper][code]
• [2021 NeurIPS] Continual World: A Robotic Benchmark For Continual Reinforcement Learning [paper]
• [2021 NeurIPS] Continual Learning via Local Module Composition [paper]
• [2021 NeurIPS] Continual Auxiliary Task Learning [paper]
• [2021 NeurIPS] Class-Incremental Learning via Dual Augmentation [paper][code]
• [2021 NeurIPS] Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [paper][code]
• [2021 NeurIPS] BooVAE: Boosting Approach for Continual Learning of VAE [paper][code]
• [2021 NeurIPS] BNS: Building Network Structures Dynamically for Continual Learning [paper]
• [2021 NeurIPS] AFEC: Active Forgetting of Negative Transfer in Continual Learning [paper]
• [2021 NeurIPS] Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning [paper][code]
• [2021 NAACL] Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution [paper]
• [2021 NAACL] Continual Learning for Text Classification with Information Disentanglement Based Regularization [paper][code]
• [2021 NAACL] Continual Learning for Neural Machine Translation [paper]
• [2021 NAACL] Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks [paper][code]
• [2021 MM] Video Transformer for Deepfake Detection with Incremental Learning [paper]
• [2021 MM] Remember and Reuse: Cross-Task Blind Image Quality Assessment via Relevance-aware Incremental Learning [paper]
• [2021 MM] Co-Transport for Class-Incremental Learning [paper][code]
• [2021 MM] An EM Framework for Online Incremental Learning of Semantic Segmentation [paper]
• [2021 IJCAI] TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning [paper][code]
• [2021 IJCAI] Learning with Selective Forgetting [paper]
• [2021 IJCAI] Knowledge Consolidation based Class Incremental Online Learning with Limited Data [paper]
• [2021 IJCAI] FedSpeech: Federated Text-to-Speech with Continual Learning [paper]
• [2021 ICPR] Semi-Supervised Class Incremental Learning [paper]
• [2021 ICPR] Class-incremental Learning with Pre-allocated Fixed Classifiers [paper][code]
• [2021 ICML] Variational Auto-Regressive Gaussian Processes for Continual Learning [paper][code]
• [2021 ICML] Kernel Continual Learning [paper][code]
• [2021 ICML] GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning [paper][code]
• [2021 ICML] Federated Continual Learning with Weighted Inter-client Transfer [paper][code]
• [2021 ICML] Continuous Coordination As a Realistic Scenario for Lifelong Learning [paper][code]
• [2021 ICML] Continual Learning in the Teacher-Student Setup Impact of Task Similarity [paper][code]
• [2021 ICML] Bayesian Structural Adaptation for Continual Learning [paper][code]
• [2021 ICLR] Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting [paper][code]
• [2021 ICLR] Linear Mode Connectivity in Multitask and Continual Learning [paper][code]
• [2021 ICLR] Gradient Projection Memory for Continual Learning [paper][code]
• [2021 ICLR] Generalized Variational Continual Learning [paper]
• [2021 ICLR] Efficient Continual Learning with Modular Networks and Task-Driven Priors [paper][code1/code2]
• [2021 ICLR] EEC: Learning to Encode and Regenerate Images for Continual Learning [paper][code]
• [2021 ICLR] CPR: Classifier-Projection Regularization for Continual Learning [paper][code]
• [2021 ICLR] Continual Learning in Recurrent Neural Networks [paper][code]
• [2021 ICLR] Contextual Transformation Networks for Online Continual Learning [paper]
• [2021 ICCV] Wanderlust: Online Continual Object Detection in the Real World [paper][code]
• [2021 ICCV] Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces [paper]
• [2021 ICCV] Striking a Balance between Stability and Plasticity for Class-Incremental Learning [paper]
• [2021 ICCV] SS-IL: Separated Softmax for Incremental Learning [paper]
• [2021 ICCV] Rehearsal revealed: The limits and merits of revisiting samples in continual learning [paper][code]
• [2021 ICCV] RECALL: Replay-based Continual Learning in Semantic Segmentation [paper][code]
• [2021 ICCV] Online Continual Learning with Natural Distribution Shifts An Empirical Study with Visual Data [paper][code]
• [2021 ICCV] Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting [paper][code]
• [2021 ICCV] Few-Shot and Continual Learning with Attentive Independent Mechanisms [paper][code]
• [2021 ICCV] Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data [paper]
• [2021 ICCV] Detection and Continual Learning of Novel Face Presentation Attacks [paper]
• [2021 ICCV] Continual Prototype Evolution Learning Online from Non-Stationary Data Streams [paper][code]
• [2021 ICCV] Continual Learning on Noisy Data Streams via Self-Purified Replay [paper]
• [2021 ICCV] Continual Learning for Image-Based Camera Localization [paper][code]
• [2021 ICCV] Co2L: Contrastive Continual Learning [paper][code]
• [2021 ICCV] Class-Incremental Learning for Action Recognition in Videos [paper]
• [2021 ICCV] Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning [paper][code]
• [2021 EMNLP] Total Recall: a Customized Continual Learning Method for Neural Semantic Parsers [paper][code]
• [2021 EMNLP] ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning [paper][code]
• [2021 EMNLP] Continual Learning in Task-Oriented Dialogue Systems [paper][code]
• [2021 EMNLP] Continual Few-Shot Learning for Text Classification [paper][code]
• [2021 EMNLP] CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks [paper][code]
• [2021 CVPR] Training Networks in Null Space of Feature Covariance for Continual Learning [paper][code]
• [2021 CVPR] Towards Open World Object Detection [paper][code]
• [2021 CVPR] Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning [paper]
• [2021 CVPR] Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning [paper][code]
• [2021 CVPR] Rectification-based Knowledge Retention for Continual Learning [paper]
• [2021 CVPR] Rainbow Memory Continual Learning with a Memory of Diverse Samples [paper][code]
• [2021 CVPR] Prototype Augmentation and Self-Supervision for Incremental Learning [paper][code]
• [2021 CVPR] PLOP: Learning without Forgetting for Continual Semantic Segmentation [paper][code]
• [2021 CVPR] ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning [paper]
• [2021 CVPR] On Learning the Geodesic Path for Incremental Learning [paper][code]
• [2021 CVPR] Lifelong Person Re-Identification via Adaptive Knowledge Accumulation [paper][code]
• [2021 CVPR] Layerwise Optimization by Gradient Decomposition for Continual Learning [paper]
• [2021 CVPR] Incremental Learning via Rate Reduction [paper]
• [2021 CVPR] Incremental Few-Shot Instance Segmentation [paper][code]
• [2021 CVPR] Image De-raining via Continual Learning [paper]
• [2021 CVPR] IIRC: Incremental Implicitly-Refined Classification [paper][code]
• [2021 CVPR] Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation [paper]
• [2021 CVPR] Few-Shot Incremental Learning with Continually Evolved Classifiers [paper][code]
• [2021 CVPR] Efficient Feature Transformations for Discriminative and Generative Continual Learning [paper][code]
• [2021 CVPR] Distilling Causal Effect of Data in Class-Incremental Learning [paper][code]
• [2021 CVPR] DER: Dynamically Expandable Representation for Class Incremental Learning [paper][code]
• [2021 CVPR] Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations [paper]
• [2021 CVPR] Continual Learning via Bit-Level Information Preserving [paper]
• [2021 CVPR] Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning [paper]
• [2021 CVPR] Adaptive Aggregation Networks for Class-Incremental Learning [paper][code]
• [2021 CVIU] SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation [paper]
• [2021 CVIU] Knowledge Distillation for Incremental Learning in Semantic Segmentation [paper]
• [2021 BMVC] Self-Supervised Training Enhances Online Continual Learning [paper]
• [2021 AISTATS] Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors [paper]
• [2021 AISTATS] A Theoretical Analysis of Catastrophic Forgetting through the NTK Overlap Matrix [paper][code]
• [2021 AAAI] Using Hindsight to Anchor Past Knowledge in Continual Learning [paper]
• [2021 AAAI] Unsupervised Model Adaptation for Continual Semantic Segmentation [paper]
• [2021 AAAI] Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network [paper][code]
• [2021 AAAI] Online Class-Incremental Continual Learning with Adversarial Shapley Value [paper][code]
• [2021 AAAI] Lifelong and Continual Learning Dialogue Systems Learning during Conversation [paper]
• [2021 AAAI] Gradient Regularized Contrastive Learning for Continual Domain Adaptation [paper]
• [2021 AAAI] Few-Shot Lifelong Learning [paper]
• [2021 AAAI] Few-Shot Class-Incremental Learning via Relation Knowledge Distillation [paper]
• [2021 AAAI] Curriculum-Meta Learning for Order-Robust Continual Relation Extraction [paper][code]
• [2021 AAAI] Continual Learning for Named Entity Recognition [paper]
• [2021 AAAI] Continual Learning by Using Information of Each Class Holistically [paper]
• [2021 AAAI] Class-Incremental Instance Segmentation via Multi-Teacher Networks [paper]
• [2021 AAAI] A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation [paper]

2020

• [2020 WACV] ScaIL: Classifier Weights Scaling for Class Incremental Learning [paper][code]
• [2020 WACV] Class-incremental Learning via Deep Model Consolidation [paper]
• [2020 TPAMI] RPSNet An Adaptive Random Path Selection Approach for Incremental Learning [paper]
• [2020 TPAMI] Continual Learning Using Bayesian Neural Networks [paper]
• [2020 PRL] Faster ILOD Incremental Learning for Object Detectors based on Faster RCNN [paper]
• [2020 NeurIPS] Understanding the Role of Training Regimes in Continual Learning [paper][code]
• [2020 NeurIPS] RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning [paper][code]
• [2020 NeurIPS] Organizing recurrent network dynamics by task-computation to enable continual learning [paper]
• [2020 NeurIPS] Online Fast Adaptation and Knowledge Accumulation (OSAKA) a New Approach to Continual Learning [paper]
• [2020 NeurIPS] Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization [paper]
• [2020 NeurIPS] Meta-Consolidation for Continual Learning [paper][code]
• [2020 NeurIPS] Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting [paper][code]
• [2020 NeurIPS] La-MAML: Look-ahead Meta Learning for Continual Learning [paper][code]
• [2020 NeurIPS] GAN Memory with No Forgetting [paper][code]
• [2020 NeurIPS] Dark Experience for General Continual Learning a Strong, Simple Baseline [paper][code]
• [2020 NeurIPS] Coresets via Bilevel Optimization for Continual Learning and Streaming [paper][code]
• [2020 NeurIPS] Continual Learning with Node-Importance based Adaptive Group Sparse Regularization [paper]
• [2020 NeurIPS] Continual Learning of Control Primitives: Skill Discovery via Reset-Games [paper][code]
• [2020 NeurIPS] Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks [paper][code1/code2]
• [2020 NeurIPS] Continual Learning in Low-rank Orthogonal Subspaces [paper][code]
• [2020 NeurIPS] Continual Deep Learning by Functional Regularisation of Memorable Past [paper][code]
• [2020 NeurIPS] Calibrating CNNs for Lifelong Learning [paper]
• [2020 Nat Comm] Brain-inspired replay for continual learning with artificial neural networks [paper][code]
• [2020 IJCNN] OvA-INN: Continual Learning with Invertible Neural Networks [paper]
• [2020 ICML] XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning [paper][code]
• [2020 ICML] Optimal Continual Learning has Perfect Memory and is NP-HARD [paper]
• [2020 ICML] Online Learned Continual Compression with Adaptive Quantization Modules [paper][code]
• [2020 ICML] Online Continual Learning from Imbalanced Data [paper]
• [2020 ICML] Neural Topic Modeling with Continual Lifelong Learning [paper][code]
• [2020 ICMLW] Wandering Within a World Online Contextualized Few-Shot Learning [paper][code]
• [2020 ICMLW] Variational Beam Search for Continual Learning [zoom]
• [2020 ICMLW] Variational Auto-Regressive Gaussian Processes for Continual Learning [paper][code]
• [2020 ICMLW] Understanding Regularisation Methods for Continual Learning [paper]
• [2020 ICMLW] UNCLEAR: A Straightforward Method for Continual Reinforcement Learning [paper]
• [2020 ICMLW] Task-Agnostic Continual Learning via Stochastic Synapses [zoom]
• [2020 ICMLW] Supermasks in Superposition [paper][code]
• [2020 ICMLW] SOLA: Continual Learning with Second-Order Loss Approximation [paper]
• [2020 ICMLW] Routing Networks with Co-training for Continual Learning [paper]
• [2020 ICMLW] On Class Orderings for Incremental Learning [paper]
• [2020 ICMLW] Deep Reinforcement Learning amidst Lifelong Non-Stationarity [paper]
• [2020 ICMLW] Continual Reinforcement Learning with Multi-Timescale Replay [paper][code]
• [2020 ICMLW] Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization [paper][code]
• [2020 ICMLW] Continual Learning from the Perspective of Compression [paper]
• [2020 ICMLW] Combining Variational Continual Learning with FiLM Layers [paper]
• [2020 ICMLW] Anatomy of Catastrophic Forgetting Hidden Representations and Task Semantics [paper]
• [2020 ICMLW] A General Framework for Continual Learning of Compositional Structures [paper]
• [2020 ICLR] Uncertainty-guided Continual Learning with Bayesian Neural Networks [paper][code]
• [2020 ICLR] Scalable and Order-robust Continual Learning with Additive Parameter Decomposition [paper][code]
• [2020 ICLR] Functional Regularisation for Continual Learning with Gaussian Processes [paper][code]
• [2020 ICLR] Continual Learning with Hypernetworks [paper][code]
• [2020 ICLR] Continual Learning with Bayesian Neural Networks for Non-Stationary Data [paper]
• [2020 ICLR] Continual Learning with Adaptive Weights (CLAW) [paper]
• [2020 ICLR] Compositional Language Continual Learning [paper][code]
• [2020 ICLR] A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning [paper][code]
• [2020 EMNLP] Visually Grounded Continual Learning of Compositional Phrases [paper][code]
• [2020 EMNLP] Disentangle-based Continual Graph Representation Learning [paper][code]
• [2020 EMNLP] Continual Learning for Natural Language Generation in Task-oriented Dialog Systems [paper]
• [2020 ECCV] Topology-Preserving Class-Incremental Learning [paper]
• [2020 ECCV] Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks [paper]
• [2020 ECCV] Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference [paper][code]
• [2020 ECCV] REMIND Your Neural Network to Prevent Catastrophic Forgetting [paper][code]
• [2020 ECCV] PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning [paper][code]
• [2020 ECCV] Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation [paper]
• [2020 ECCV] Online Continual Learning under Extreme Memory Constraints [paper][code]
• [2020 ECCV] More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning [paper][code]
• [2020 ECCV] Memory-Efficient Incremental Learning Through Feature Adaptation [paper]
• [2020 ECCV] Learning latent representations across multiple data domains using Lifelong VAEGAN [paper][code]
• [2020 ECCV] Incremental Meta-Learning via Indirect Discriminant Alignment [paper]
• [2020 ECCV] Imbalanced Continual Learning with Partitioning Reservoir Sampling [paper]
• [2020 ECCV] GDumb: A Simple Approach that Questions Our Progress in Continual Learning [paper][code]
• [2020 ECCV] Class-Incremental Domain Adaptation [paper]
• [2020 ECCV] Adversarial Continual Learning [paper][code]
• [2020 ECAI] Learning to Continually Learn [paper]
• [2020 CVPR] Semantic Drift Compensation for Class-Incremental Learning [paper][code]
• [2020 CVPR] Modeling the Background for Incremental Learning in Semantic Segmentation [paper]
• [2020 CVPR] Mnemonics Training: Multi-Class Incremental Learning without Forgetting [paper][code]
• [2020 CVPR] Maintaining Discrimination and Fairness in Class Incremental Learning [paper]
• [2020 CVPR] iTAML: An Incremental Task-Agnostic Meta-learning Approach [paper][code]
• [2020 CVPR] Incremental Learning In Online Scenario [paper]
• [2020 CVPR] Incremental Few-Shot Object Detection [paper]
• [2020 CVPR] Few-Shot Class-Incremental Learning [paper]
• [2020 CVPR] Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion [paper][code]
• [2020 CVPR] Continual Learning with Extended Kronecker-factored Approximate Curvature [paper]
• [2020 CVPR] Conditional Channel Gated Networks for Task-Aware Continual Learning [paper]
• [2020 CVPRW] What is Happening Inside a Continual Learning Model: A Representation-Based Evaluation of Representational Forgetting [paper]
• [2020 CVPRW] Stream-51: Streaming Classification and Novelty Detection from Videos [paper]
• [2020 CVPRW] StackNet: Stacking feature maps for Continual learning [paper]
• [2020 CVPRW] Relationship Matters Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors [paper]
• [2020 CVPRW] Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches [paper]
• [2020 CVPRW] Reducing catastrophic forgetting with learning on synthetic data [paper]
• [2020 CVPRW] Noise-Based Selection of Robust Inherited Model for Accurate Continual Learning [paper]
• [2020 CVPRW] Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis [paper][code]
• [2020 CVPRW] Generative Feature Replay For Class-Incremental Learning [paper][code]
• [2020 CVPRW] Generating Accurate Pseudo Examples for Continual Learning [paper]
• [2020 CVPRW] Generalized Class Incremental Learning [paper]
• [2020 CVPRW] Dropout as an Implicit Gating Mechanism For Continual Learning [paper][code][code]
• [2020 CVPRW] Continual Reinforcement Learning in 3D Non-stationary Environments [paper][code][code]
• [2020 CVPRW] Continual Learning of Object Instances [paper]
• [2020 CVPRW] Continual Learning for Anomaly Detection in Surveillance Videos [paper]
• [2020 CVPRW] Cognitively-Inspired Model for Incremental Learning Using a Few Examples [paper][cdde]
• [2020 CVPRW] CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition [paper]
• [2020 COLING] Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation [paper]
• [2020 COLING] Distill and Replay for Continual Language Learning [paper]
• [2020 COLING] Continual Lifelong Learning in Natural Language Processing: A Survey [paper]
• [2020 COLING] A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification [paper]
• [2020 BMVC] Initial Classifier Weights Replay for Memoryless Class Incremental Learning [paper][code]
• [2020 AISTATS] Orthogonal Gradient Descent for Continual Learning [paper]
• [2020 ACL] Continual Relation Learning via Episodic Memory Activation and Reconsolidation [paper]
• [2020 AAAI] Residual Continual Learning [paper]
• [2020 AAAI] Overcoming Catastrophic Forgetting by Neuron-Level Plasticity Control [paper]
• [2020 AAAI] Learning from the Past: Continual Meta-Learning with Bayesian Graph Neural Networks [paper]
• [2020 AAAI] Generative Continual Concept Learning [paper]
• [2020 AAAI] ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding [paper][code]
• [2020 AAAI] Bi-Objective Continual Learning: Learning ‘New’ While Consolidating ‘Known’ [paper]

2019

• [2019 NIPS] Uncertainty-based Continual Learning with Adaptive Regularization [paper][code]
• [2019 NIPS] RPSNet: Random Path Selection for Incremental Learning [paper]
• [2019 NIPS] Reconciling meta-learning and continual learning with online mixtures of tasks [paper]
• [2019 NIPS] Online Continual Learning with Maximally Interfered Retrieval [paper][code]
• [2019 NIPS] Meta-Learning Representations for Continual Learning [paper][code]
• [2019 NIPS] Incremental Few-Shot Learning with Attention Attractor Networks [paper][code]
• [2019 NIPS] Gradient based sample selection for online continual learning [paper][code]
• [2019 NIPS] Experience Replay for Continual Learning [paper]
• [2019 NIPS] Episodic Memory in Lifelong Language Learning [paper]
• [2019 NIPS] Compacting, Picking and Growing for Unforgetting Continual Learning [paper][code]
• [2019 NeurIPS] Incremental Few-Shot Learning with Attention Attractor Networks [paper][code]
• [2019 Nat Mat Int] Continual learning of context-dependent processing in neural networks [paper][code]
• [2019 NAACL] Continual Learning for Sentence Representations Using Conceptors [paper][code]
• [2019 IJCAI] Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay [paper]
• [2019 ICML] Policy Consolidation for Continual Reinforcement Learning [paper]
• [2019 ICML] Learn to Grow A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting [paper]
• [2019 ICME] An End-to-End Architecture for Class-Incremental Object Detection with Knowledge Distillation [paper]
• [2019 ICLR] Overcoming Catastrophic Forgetting for Continual Learning via Model Adaptation [paper]
• [2019 ICLR] Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference [paper][code]
• [2019 ICLR] Efficient Lifelong Learning with A-GEM [paper][code]
• [2019 ICLR] A Comprehensive, Application-Oriented Study of Catastrophic Forgetting in DNNs [paper]
• [2019 ICCV] Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild [paper][code]
• [2019 ICCV] Lifelong GAN: Continual Learning for Conditional Image Generation [paper]
• [2019 ICCV] Incremental Learning Using Conditional Adversarial Networks [paper]
• [2019 ICCV] IL2M: Class Incremental Learning With Dual Memory [paper]
• [2019 ICCV] Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation [paper]
• [2019 ICCVW] Incremental Learning Techniques for Semantic Segmentation [paper]
• [2019 EMNLP] A Progressive Model to Enable Continual Learning for Semantic Slot Filling [paper]
• [2019 CVPR] Task-Free Continual Learning [paper]
• [2019 CVPR] Learning without Memorizing [paper]
• [2019 CVPR] Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning [paper]
• [2019 CVPR] Learning a Unified Classifier Incrementally via Rebalancing [paper]
• [2019 CVPR] Large Scale Incremental Learning [paper]
• [2019 ACL] Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering [paper]
• [2019 ACL] Incremental Learning from Scratch for Task-Oriented Dialogue Systems [paper][code]
• [2019 AAAI] Scalable Recollections for Continual Lifelong Learning [paper]

2018

• [2018 NIPS] Reinforced Continual Learning [paper]
• [2018 NIPS] Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines [paper][code]
• [2018 NIPS] Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting [paper]
• [2018 NIPS] Memory Replay GANs: learning to generate images from new categories without forgetting [paper][code]
• [2018 NIPS] Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies [paper]
• [2018 NIPSW] Three scenarios for continual learning [paper][code]
• [2018 NIPSW] Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines [paper][code]
• [2018 ICPR] Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting [paper][code]
• [2018 ICML] Progress & Compress: A scalable framework for continual learning [paper]
• [2018 ICML] Overcoming Catastrophic Forgetting with Hard Attention to the Task [paper][[code](https://github.com/joansj/hat]
• [2018 ICML] Continual Reinforcement Learning with Complex Synapses [paper]
• [2018 ICLR] Variational Continual Learning [paper]
• [2018 ICLR] Lifelong Learning with Dynamically Expandable Networks [paper]
• [2018 ICLR] FearNet: Brain-Inspired Model for Incremental Learning [paper]
• [2018 ECCV] Riemannian Walk for Incremental Learning Understanding Forgetting and Intransigence [paper][code]
• [2018 ECCV] Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights [paper][code]
• [2018 ECCV] Memory Aware Synapses Learning what (not) to forget [paper]
• [2018 ECCV] Lifelong Learning via Progressive Distillation and Retrospection [paper]
• [2018 ECCV] End-to-End Incremental Learning [paper]
• [2018 CVPR] PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning [paper][code]
• [2018 BMVC] Exemplar-Supported Generative Reproduction for Class Incremental Learning [paper]
• [2018 AAAI] Selective Experience Replay for Lifelong Learning [paper]

2017

• [2017 arXiv] PathNet: Evolution Channels Gradient Descent in Super Neural Networks [paper]
• [2017 arXiv] Continual Learning in Generative Adversarial Nets [paper]
• [2017 PNAS] Overcoming catastrophic forgetting in neural networks [paper]
• [2017 NIPS] Overcoming Catastrophic Forgetting by Incremental Moment Matching [paper][code]
• [2017 NIPS] Gradient Episodic Memory for Continual Learning [paper][code]
• [2017 NIPS] Continual Learning with Deep Generative Replay [paper]
• [2017 ICML] Continual Learning Through Synaptic Intelligence [paper][code]
• [2017 ICCV] Incremental Learning of Object Detectors without Catastrophic Forgetting [paper]
• [2017 ICCV] Encoder Based Lifelong Learning [paper]
• [2017 CVPR] iCaRL: Incremental Classifier and Representation Learning [paper][code]
• [2017 CVPR] Expert Gate: Lifelong Learning with a Network of Experts [paper]
• [2017 CoRL] CORe50: a New Dataset and Benchmark for Continuous Object Recognition [paper]

2016 and earlier

• [2016 arXiv] Progressive Neural Networks [paper]
• [2016 ECCV] Learning without Forgetting [paper]
• [2014 ICML] A PAC-Bayesian Bound for Lifelong Learning [paper]

Citation

Please cite our paper if it is helpful to your work:

@article{wang2023comprehensive,
  title={A comprehensive survey of continual learning: Theory, method and application},
  author={Wang, Liyuan and Zhang, Xingxing and Su, Hang and Zhu, Jun},
  journal={arXiv preprint arXiv:2302.00487},
  year={2023}
}