Skip to content

liruihui/Reading_List

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

165 Commits
README.md
README.md
 
 

Repository files navigation

Reading_List

Reading list on deep learning.

Tutorial

  • SSL: Thang Luong. "Learning from Unlabeled Data".

Survey

  • Geometric Primitives: Shaobo Xia et al."Geometric Primitives in LiDAR Point Clouds: A Review"

  • Surface Reconstrcution: Matthew Berger et al."A Survey of Surface Reconstruction from Point Clouds". CGF 2016

  • 3D Survey: Ying Li et al. "Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review" arXiv:2005.09830 (Waterloo)

  • 3D Survey: Yulan Guo, et al. "Deep Learning for 3D Point Clouds: A Survey" arXiv:1912.12033 (NUDT)

  • I2I Survey: Yingxue Pang et al. "Image-to-Image Translation: Methods and Applications" arXiv 2101.08629 (USTC)

  • GAN Survey: Zhengwei Wang et al. "Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy" ACM Computing Surveys 2020 (Trinity College Dublin)

  • GAN Survey: Abdul Jabbar et al. "A Survey on Generative Adversarial Networks:Variants, Applications, and Training". 2020 (ZJU)

  • ViT Survey: Kai Han et al. "A Survey on Visual Transformer" arXiv 2012.12556 (Noah)

  • ViT Survey: Salman Khan et al. "Transformers in Vision: A Survey" arXiv 2101.01169 (MBZ)

Metric learning

  • PointMixup: Yunlu Chen et al. "PointMixup: Augmentation for Point Clouds" In ECCV 2020 (University of Amsterdam)

  • Embedding Expansion: Byungsoo Ko et al. "Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning" In CVPR 2020 (NAVER)

  • BALMS: Jiawei Ren et al."Balanced Meta-Softmax for Long-Tailed Visual Recognition" In NIPS 2020 (NTU)

Biomolecule Modelling

  • dMaSIF: Freyr Sverrisson et al."Fast End-to-End Learning on Protein Surfaces". CVPR 2021 (EPFL)

3D Mesh && PointCloud

  • VSL: Shikun Liu et al."Learning a Hierarchical Latent-Variable Model of 3D Shapes" 3DV 2018 (ICL)

  • IMLSNet: Shi-Lin Liu et al."Deep Implicit Moving Least-Squares Functions for 3D Reconstruction" CVPR 2021 (MSRA)

  • GeoNet: Tong He et al."GeoNet: Deep Geodesic Networks for Point Cloud Analysis" CVPR 2019 (UCLA)

  • Spare-Net: Chulin Xie et al. "Style-based Point Generator with Adversarial Rendering for Point Cloud Completion". CVPR 2021 (UIUC)

  • GPDNet: Pistilli, Francesca et al. "Learning Graph-Convolutional Representationsfor Point Cloud Denoising". ECCV 2020 (Politecnico di Torino, Italy)

  • DMRDenoise: Luo, Shitong et al. "Differentiable Manifold Reconstruction for Point Cloud Denoising". ACMMM 2021 (PKU)

  • Rethinking Sampling: He Wang et al. "Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks" arXiv:2006.07029 (Standford)

  • UDS: Yi Shi et al. "Unsupervised Deep Shape Descriptor with Point Distribution Learning" In CVPR 2020 (NYU)

  • MS-cGAN: Rundi Wu et al. "Multimodal Shape Completion via Conditional Generative Adversarial Networks" In ECCV 2020 (PKU)

  • LG-GAN: Hang Zhou et al. "LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud Based Deep Networks" In CVPR 2020 (USTC)

  • DUP-Net: Hang Zhou et al. "DUP-Net: Denoiser and Upsampler Network for 3D Adversarial Point Clouds Defense" In ICCV 2019 (USTC)

  • GvG-P: Xiaoyi Dong et al. "Self-Robust 3D Point Recognition via Gather-Vector Guidance" In CVPR 2020 (USTC)

  • PosPool: Ze Liu et al."A Closer Look at Local Aggregation Operators in Point Cloud Analysis" ECCV 2020 (NSTC)

  • PointNL: Mingmei Cheng et al. " Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation " IROS 2020 (NUST)

  • PointContrast: Saining Xie et al. "PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding" ECCV 2020 (FAIR)

  • SVCN: Li Yi et al. "Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds". arXiv 2007 (Google)

  • PDGN: Le Hui et al. "Progressive Point Cloud Deconvolution Generation Network". ECCV 2020 (NJUST)

  • ACNe: Weiwei Sun et al. "ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning". CVPR 2020 (University of Victoria)

  • Spatial Transformer: Jiayun Wang et al. "Spatial Transformer for 3D Point Clouds" arXiv:1906.10887 (UC Berkeley)

  • MOPS-Net: Yue Qian et al. "MOPS-Net: A Matrix Optimization-driven Network for Task-Oriented 3D Point Cloud Downsampling" arXiv:2005.00383 (CityU)

  • C-Flow: Albert Pumarola et al. "C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds". In CVPR 2020 (Google)

  • PointGMM: Amir Hertz et al. "PointGMM: a Neural GMM Network for Point Clouds". In CVPR 2020 (TAU)

  • GLR: Yongming Rao et al. "Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds". In CVPR 2020 (THU)

  • Grid-GCN: Qiangeng Xu et al. "Grid-GCN for Fast and Scalable Point Cloud Learning". In CVPR 2020 (USC)

  • TreeGAN: Dong Wook Shu et al. "3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions". In ICCV 2019 (CAU)

  • SA-Net: Xin Wen et al. "Point Cloud Completion by Skip-attention Network with Hierarchical Folding". arXiv:2005.03871 (THU)

  • Cascaded: Xiaogang Wang et al. "Cascaded Refinement Network for Point Cloud Completion". In CVPR 2020 (NUS)

  • PF-Net: Zitian Huang, et al. "PF-Net: Point Fractal Network for 3D Point Cloud Completion" In CVPR 2020 (SJTU)

  • FPConv: Yiqun Lin et al. "FPConv: Learning Local Flattening for Point Convolution". In CVPR 2020 (CHKU SZ)

  • 3DStructurePoints: Nenglun Chen et al. "Unsupervised Learning of Intrinsic Structural Representation Points". In CVPR 2020 (HKU)

  • DPDist: Dahlia Urbach et al. "DPDist:Comparing Point Clouds Using Deep Point Cloud Distance" arXiv:2004.11784

  • Mesh R-CNN: Georgia Gkioxari et al. "Mesh R-CNN." In ICCV 2019

  • TMNet: Junyi Pan et al. "Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks." In ICCV 2019

  • Pixel2Mesh++: Chao Wen et al. "Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation." In ICCV 2019

  • Pixel2Mesh: Nanyang Wang et al. "Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images." In ECCV 2018

  • Scan2Mesh: Angela Dai, et al. "Scan2Mesh: From Unstructured Range Scans to 3D Meshes" In ICCV 2019

  • PointGroup: Jiang Li, et al. "PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation" In CVPR2020

  • DetDA: Martin Hahner, et al. "Quantifying Data Augmentation for LiDAR based 3D Object Detection" arXiv:2004.01643

  • DCM-Net: Jonas Schult, et al. "DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes" In CVPR2020

  • PCRNet: Vinit Sarode, et al. "PCRNet: Point Cloud Registration Network using PointNet Encoding" arXiv:1908.07906.

  • PointNetLK: Yasuhiro Aoki, et al. "PointNetLK: Robust & Efficient Point Cloud Registration using PointNet" In 2019 CVPR

  • DPAM: Jinxian Liu, et al. "Dynamic Points Agglomeration for Hierarchical Point Sets Learning" In 2019 ICCV

  • PointNet: Charles R. Qi, et al. "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" In 2017 CVPR

  • PointNet++: Charles R. Qi, et al. "PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space" In 2017 NIPS

  • FrustumNet: Charles R. Qi, et al. "Frustum PointNets for 3D Object Detection from RGB-D Data" In 2018 CVPR

  • 3D-GAN: Jiajun Wu, et al."Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling" In 2016 NIPS

  • IW-GAN:Edward J. Smith, et al. "Improved Adversarial Systems for 3D Object Generation and Reconstruction" arXiv:1707.09557.

  • RecGAN: Bo Yang, et al. "3D Object Reconstruction from a Single Depth View with Adversarial Learning" In 2017 ICCV workshop

  • GAL: Jiang Li, et al. "GAL: Geometric Adversarial Loss for Single-View 3D-Object Reconstruction" In 2018 ECCV

  • PSGN: Haoqiang Fan, et al."A Point Set Generation Network for 3D Object Reconstruction from a Single Image" In 2017 CVPR

  • PCN: Wentao Yuan, et al. "PCN: Point Completion Network." In 2018 3DV

  • Dense Rec: Chen-Hsuan Lin, et al."Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction" In 2018 AAAI

  • Point Cloud GAN: Chun-Liang Li, et al. "Point Cloud GAN" arXiv:1810.05795.

  • PCPNet: Paul Guerrero, et al. "PCPNET: Learning Local Shape Properties from Raw Point Clouds" In 2018 EG

  • LRGM: Panos Achlioptas, et al."Learning Representations and Generative Models for 3D Point Clouds" In 2018 ICLR workshop

  • P2PNet: KANGXUE YIN, et al."P2P-NET: Bidirectional Point Displacement Net for Shape Transform" In 2018 Siggraph

  • POINTCLEANNET: Marie-Julie Rakotosaona, et al. "POINTCLEANNET: Learning to Denoise and Remove Outliers from Dense Point Clouds" arXiv: 1901.01060

  • PPFNet: Haowen Deng, et al. "PPFNet: Global Context Aware Local Features for Robust 3D Point Matching" In 2018 CVPR

  • PPF-FoldNet: Haowen Deng, et al. "PPF-FoldNet: Unsupervised Learnning of Rotation Invariant 3D Local Descriptors" In 2018 ECCV

  • PPPU: Wang Yifan, et al. "Patch-based Progressive 3D Point Set Upsampling " In 2019 CVPR

  • L2S: Oren Dovrat, et al. "Learning to Sample" In 2019 CVPR

  • PCC: Xuelin Chen, et al. "Unpaired Point Cloud Completion on Real Scans using Adversarial Training" arXiv:1904.00069

  • PointConv: Wenxuan Wu, et al. "PointConv: Deep Convolutional Networks on 3D Point Clouds". In 2019 CVPR

  • PointCNN: Yangyan Li, et al. "PointCNN: Convolution On X-Transformed Points". In 2018 NIPS

  • PointWeb: Hengshuang Zhao, et al. "PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing" In 2019 CVPR

  • Point-Capsule: Yongheng Zhao, et al. "3D Point-Capsule Networks" arXiv:1812.10775

  • CPL: Ehsan Nezhadarya, et al. "Adaptive Hierarchical Down-Sampling for Point Cloud Classification" arXiv:1904.08506

  • PCAN: Wenxiao Zhang, et al. "PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval" In 2019 CVPR

  • PN_VLAD: Mikaela Angelina Uy, et al. "PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition" In 2018 CVPR

  • RS-CNN: Yongcheng Liu, et al. "Relation-Shape Convolutional Neural Network for Point Cloud Analysis" In 2019 CVPR

  • GPC: Chong Xiang, et al. "Generating 3D Adversarial Point Clouds" In 2019 CVPR

  • SO-NET: Jiaxin Li, et al. "SO-Net: Self-Organizing Network for Point Cloud Analysis" In 2018 CVPR

  • PU-Net: Lequan Yu, et al. "PU-Net: Point Cloud Upsampling Network" In 2018 CVPR

  • EC-Net: Lequan Yu, et al. "EC-Net: an Edge-aware Point set Consolidation Network" In 2018 ECCV

  • LSGC: Chu Wang, et al. "Local Spectral Graph Convolution for Point Set Feature Learning" In 2018 ECCV

  • Gconv: Diego Valsesia, et al. " Learning Localized Generative Models for 3D Point Clouds via Graph Convolution" In 2019 ICLR

  • Scan2Mesh: Angela Dai, et al. "Scan2Mesh: From Unstructured Range Scans to 3D Meshes." In 2019 CVPR

  • ContrastNet: Ling Zhang, et al. "Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network" arXiv:1904.12359

  • VoteNet: Charles R. Qi, et al. "Deep Hough Voting for 3D Object Detection in Point Clouds" In ICCV 2019

  • StructureNet: Zhidong Liang, et al. "3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation" arXiv:1902.05247

  • DeepGCN: Guohao Li, et al. "Can GCNs Go as Deep as CNNs?" arXiv:1904.03751

  • RGCNN: Gusi Te, et al. "RGCNN: Regularized Graph CNN for Point Cloud Segmentation" In 2018 ACM MM

  • SRN: Yueqi Duan, et al. "Structural Relational Reasoning of Point Clouds" In 2019 CVPR

  • Tree-GAN: Dong Wook Shu, et al. "3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions" arXiv:1905.06292

  • KPConv: Hugues Thomas, et al. “KPConv: Flexible and Deformable Convolution for Point Clouds” In ICCV 2019

  • PointWise:Matan Shoef, et al. "PointWise: An Unsupervised Point-wise Feature Learning Network" arXiv:1901.04544

  • GAPNet: Can Chen, et al. "GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud" arXiv:1905.08705

  • GACNet: Lei Wang, et al. "Graph Attention Convolution for Point Cloud Segmentation" In CVPR 2019

  • DensePCR: Priyanka Mandikal, et al. "Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network" arXiv:1901.08906

  • G-NPC: Xianzhi Li, et al. "Non-Local Low-Rank Normal Filtering for Mesh Denoising" In 2018 PG

  • AGCN: Zhuyang Xie, et al. "Point Clouds Learning with Attention-based Graph Convolution Networks" arXiv: 1905.13445

  • MeshNet: Yutong Feng, et al. "MeshNet: Mesh Neural Network for 3D Shape Representation" arXiv: 1811.11424

  • TopNet: Lyne P. Tchapmi et al."TopNet: Structural Point Cloud Decoder." In CVPR 2019

  • Nesti-Net: Yizhak Ben-Shabat et al."Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks." In CVPR 2019

  • LOGAN: Kangxue Yin et al."LOGAN: Unpaired Shape Transform in Latent Overcomplete Space." In Siggraph Asia 2019

  • PointFlow: Guandao Yang et al. "PointFlow : 3D Point Cloud Generation with Continuous Normalizing Flow" In ICCV 2019

  • InterpCNN: Jiageng Mao et al. "Interpolated Convolutional Networks for 3D Point Cloud Understanding" In ICCV 2019

  • Point-Edge: Li Jiang et al. "Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation" In ICCV 2019

  • RI-Conv: Zhiyuan Zhang et al. "Rotation Invariant Convolutions for 3D Point Clouds Deep Learning" In CVPR 2019

  • Shape Unicode: Sanjeev Muralikrishnan et al. "Shape Unicode: A Unified Shape Representation" In CVPR 2019

  • BPS: Sergey Prokudin et al. "Efficient Learning on Point Clouds with Basis Point Sets." In ICCV 2019

  • ShellNet: Zhiyuan Zhang et al. "ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics." In ICCV 2019

  • SS-NET: Jonathan Sauder et al."Self-Supervised Deep Learning on Point Clouds by Reconstructing Space." In NIPS 2019

  • Attentional EdgeConv: Qiu Shi et al."Geometric Feedback Network for Point Cloud Classification." arXiv preprint arXiv:1911.12885 (2019).

  • Morphing-Net: Minghua Liu et al."Morphing and Sampling Network for Dense Point Cloud Completion." In AAAI 2020

  • PCC-GAN: Xuelin Chen et al."Unpaired Point Cloud Completion on Real Scans using Adversarial Training." In ICML 2019.

  • Point2Node: Wenkai Han et al."Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling." arXiv preprint arXiv:1912.10775 (2019).

  • GS-Net: Mingye Xu et al."Geometry Sharing Network for 3D Point Cloud Classification and Segmentation." In AAAI 2020.

2D Image

  • IMCL:Dat el al. Interactive Multi-Label CNN Learning with Partial Labels. CVPR 2020.

  • PISE:Jinsong Zhang et al. "PISE: Person Image Synthesis and Editing with Decoupled GAN" Tianjin University. CVPR 20201

  • Im2Vec:Pradyumna Reddy et al. "Im2Vec: Synthesizing Vector Graphics without Vector Supervision" UCL. CVPR 20201

  • Infinite Nature:Andrew Liu et al. "Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image" Google. arxiv 2021

  • AniGAN:Bing Li et al. " AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation" KAUST. arxiv 2021

  • CS-DisMo:Xuanchi Ren et al. "Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglemen" HKUST. arxiv 2021

  • DSA: Bo Zhao et al. "Dataset Condensation with Differentiable Siamese Augmentation". University of Edinburgh. ICML 2021

  • DiffAug: Shengyu Zhao et al. "Differentiable Augmentation for Data-Efficient GAN Training". THU. 2020 NIPS

  • Deformable DETR: Xizhou Zhu et al."Deformable DETR: Deformable Transformers for End-to-End Object Detection". SenseTime. 2021 ICLR.

  • TransGAN: Yifan Jiang et al. "TransGAN: Two Transformers Can Make One Strong GAN". Texas University. 2021 ICML.

  • ViT: Alexey, Dosovitskiy et al. "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale". Google Brain. 2021 ICLR.

  • iGPT: Alec Radford, et al. "Generative Pretraining From Pixels". OpenAI. 2020 ICML.

  • OASIS: Edgar Schonfeld, et al. "You Only Need Adversarial Supervision for Semantic Image Synthesis." BOSCH. 2021 ICLR.

  • U-Net GAN: Edgar Schonfeld, et al. "A U-Net Based Discriminator for Generative Adversarial Networks." BOSCH. 2020 CVPR.

  • GauGAN/SPADE: Taesung Park, et al."Semantic Image Synthesis with Spatially-Adaptive Normalization" NVIDIA. 2019 CVPR.

  • BigGAN: Andrew Brock, et al. "Large Scale GAN Training for High Fidelity Natural Image Synthesis" DeepMind. ICLR 2019

  • Few-Shot StyleGAN: Tero Karras, et al. "Training generative adversarial networks with limited data." NVIDIA. arXiv:2006.06676.

  • Semi-StyleGAN: Tero Karras, et al. "Semi-Supervised StyleGAN for Disentanglement Learning." NVIDIA. 2020 ICML.

  • StyleGAN2: Tero Karras, et al. "Analyzing and Improving the Image Quality of StyleGAN." NVIDIA. 2020 CVPR.

  • StyleGAN: Tero Karras , et al. "A Style-Based Generator Architecture for Generative Adversarial Networks ." NVIDIA. 2019 CVPR.

  • Tangent Images: Marc Eder et al. "Tangent Images for Mitigating Spherical Distortion" CVPR 2020 (UNC)

  • iSeeBetter: Aman Chadha et al. "iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks" (Standford)

  • Triplet Attention: Diganta Misra et al. "Rotate to Attend: Convolutional Triplet Attention Module" arXiv 2010.03045(Landskape)

  • SSN: Wenqi Shao et al. "SSN: Learning Sparse Switchable Normalization via SparsestMax" CVPR 2019 (CUHK)

  • SAOL: Ildoo Kim et al. "Spatially Attentive Output Layer for Image Classification" CVPR 2020 (Kakao Brain)

  • IOFPL: Markus Hofinger et al. "Improving Optical Flow on a Pyramid Level" ECCV 2020 (Facebook)

  • RAFT: Zachary Teed et al. "RAFT: Recurrent All-Pairs Field Transforms for Optical Flow" ECCV 2020 (Princeton University)

  • Shape Adaptor: Shikun Liu et al."Shape Adaptor: A Learnable Resizing Module". ECCV 2020 (Imperial College London)

  • FONTS: David Stutz et al."Disentangling Adversarial Robustness and Generalization" CVPR2019(University of Tubingen)

  • TI-FGSM: Yinpeng Dong et al."Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks" CVPR2019(THU)

  • CUT: Taesung Park et al."Contrastive Learning for Unpaired Image-to-Image Translation" ECCV2020(UCB)

  • GAN Prior: Jinjin Gu et al. "Image Processing Using Multi-Code GAN Prior". CVPR2020(CUHK)

  • PriorGAN: Shuyang Gu et al. "PriorGAN: Real Data Prior for Generative Adversarial Nets". arXiv 2006.16990 (USTC)

  • PULSE: Sachit Menon et al. "PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models". CVPR2020(Duke)

  • Relation-Net: Han Hu et al. "Relation networks for object detection". CVPR 2018(MSRA)

  • Stand-Alone: Prajit Ramachandran et al. "Stand-Alone Self-Attention in Vision Models" In NIPS 2019 (Google)

  • AA-Net: Irwan Bello et al. "Attention Augmented Convolutional Networks" In ICCV 2019 (Google)

  • ISRN: Yuqing Liu et al. "Iterative Network for Image Super-Resolution" arXiv: 2005.09964 (DLUT)

  • VAE+Flow: Xuezhe Ma1 et al. "Decoupling Global and Local Representations from/for Image Generation" arXiv: 2004.11820

  • SAOL: Ildoo Kim et al. “Spatially Attentive Output Layer for Image Classification." In CVPR 2020

  • Circle Loss: Yifan Sun et al."Circle Loss: A Unified Perspective of Pair Similarity Optimization." In CVPR 2020

  • DeepSnake: Sida Peng et al."Deep Snake for Real-Time Instance Segmentation." In CVPR 2020

  • DRN: Guo, Yong, et al. "Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution." In CVPR 2020.

  • MSG-GAN: Animesh Karnewar, et al. "MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks." In CVPR 2020.

  • StructuredGAN: Irad Peleg, et al. "Structured GANs." arXiv:2001.05216.

  • GAN: Goodfellow, Ian, et al. "Generative adversarial nets." In 2014 NIPS .

  • cGAN: Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784 (2014).

  • S2-GAN: Xiaolong Wang, et al. "Generative Image Modeling using Style and Structure Adversarial Networks." In 2016 ECCV

  • DCGAN: Radford, et al.. "Unsupervised representation learning with deep convolutional generative adversarial networks." In 2016 ICLR

  • Pixel2Pixel: Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." In 2017 CVPR

  • SRGAN: Christian,Ledig, et al. "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network." In 2017 CVPR.

  • AdaIN: Xun Huang , et al. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization ." In 2017 ICCV

  • CycleGAN：Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." In 2017 ICCV.

  • BicycleGAN：Zhu, Jun-Yan, et al. "Toward Multimodal Image-to-Image Translation." In 2017 NIPS.

  • Least square loss :Xudong, Mao , et al. "Least Squares Generative Adversarial Networks ." In 2017 ICCV.

  • StackGAN: Han Zhang, et al. "StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks ." In 2017 ICCV.

  • StackGAN++: Han Zhang, et al. "StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks ." In 2018 TPAMI.

  • TTUR: Martin Heusel, et al. "GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium ." In 2017 NIPS.

  • SGAN: Xun Huang , et al. "Stacked Generative Adversarial Networks ." In 2017 CVPR.

  • StarGAN: Choi, Yunjey, et al. "Stargan: Unified generative adversarial networks for multi-domain image-to-image translation." In CVPR 2018.

  • DISSECTIONGAN: Zhu, Jun-Yan, et al. "GAN DISSECTION: VISUALIZING AND UNDERSTANDING GENERATIVE ADVERSARIAL NETWORKS ." arXiv:1811.10597.

  • GDWTC: Wonwoong Cho, et al. "Image-to-Image Translation via Group-wise Deep Whitening and Coloring Transformation" arXiv:1812.09912.

  • Latent Filter GAN: Yazeed Alharbi, et al."Latent Filter Scaling for Multimodal Unsupervised Image-to-Image Translation." In CVPR 2019.

  • CariGAN: KAIDI CAO, et al. "CariGANs: Unpaired Photo-to-Caricature Translation." In 2018 Siggraph.

  • AttnGAN: Tao Xu, et al. "AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks." In 2018 CVPR

  • Pixel2PixelHD: Ting-Chun Wang, et al. "High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs." In 2018 CVPR

  • Attention-GAN: Xinyuan Chen, et al. "Attention-GAN for Object Transfiguration in Wild Images." In 2018 ECCV

  • DRIT: Hsin-Ying Lee, et al. "Diverse Image-to-Image Translation via Disentangled Representations." In 2018 ECCV

  • Contextual GAN: Yongyi Lu, et al. "Image Generation from Sketch Constraint Using Contextual GAN." In 2018 ECCV

  • MUINT: Xun Huang, et al. "Multimodal Unsupervised Image-to-Image Translation" In 2018 ECCV

  • Recycle-GAN: Aayush Bansal , et al. "Recycle-GAN: Unsupervised Video Retargeting" In 2018 ECCV

  • Sub-GAN: Jie Liang, et al."Sub-GAN: An Unsupervised Generative Model via Subspaces" In 2018 ECCV

  • Stack CycleGAN:Minjun Li, et al. "Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks " In 2018 ECCV

  • Progressive GAN: Tero Karras , et al."PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION" In 2018 ICLR

  • SAGAN: Han Zhang, et al. "Self-Attention Generative Adversarial Networks" In 2018 ICLR

  • SN: Takeru Miyato, et al. "SPECTRAL NORMALIZATION FOR GENERATIVE ADVERSARIAL NETWORKS " In 2018 ICLR

  • Augmented CycleGAN: Amjad Almahairi , et al. "Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data" In 2018 ICML

  • BigGAN: Andrew Brock, et al."LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS " In ICLR 2019

  • InstaGAN: Sangwoo Mo, et al. "INSTANCE-AWARE IMAGE-TO-IMAGE TRANSLATION " In 2019 ICLR

  • RGAN: Alexia Jolicoeur-Martineau, et al. "The relativistic discriminator: a key element missing from standard GAN" In 2019 ICLR

  • Pixel-Shuffer: Wenzhe Shi, et al. "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network" In CVPR 2016

  • Degradation GAN: Adrian Bulat et al."To learn image super-resolution, use a GAN to learn how to do image degradation first." In ECCV 2018

  • DANet: Jun Fu et al. "Dual Attention Network for Scene Segmentation." In CVPR 2019

  • FineGAN: Krishna Kumar Singh et al. "FineGAN: Unsupervised Hierarchical Disentanglement for Fine-Grained Object Generation and Discovery." In CVPR 2019

  • TransGaGa: Wayne Wu et al. "TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation." In CVPR 2019

  • Deblur-GAN: Boyu Lu et al. "Unsupervised Domain-Specific Deblurring via Disentangled Representations." In CVPR 2019

  • NoiseFlow: Abdelrahman Abdelhamed et al. "Noise Flow: Noise Modeling with Conditional Normalizing Flows." arXiv:1908.08453

  • DMIT: Xiaoming Yu et al. "Multi-mapping Image-to-Image Translation via Learning Disentanglement." In NIPS 2019

  • Stylized-ImageNet: Robert Geirhos et al. "ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness." In ICLR 2019

  • InvertGray: Xia, Menghan et al. "Invertible grayscale." In SIGGRAPH Asia 2018

  • DegradeSR: Bulat, Adrian et al. "To learn image super-resolution, use a GAN to learn how to do image degradation first." In ECCV 2018

Network

  • EfficientDet: Tan, Mingxing et al. "EfficientDet: Scalable and Efficient Object Detection." arXiv: 1911.09070

  • STDL: Peng Zhou et al. "Scale-Transferrable Object Detection." In CVPR 2018.

  • DARTS: Hanxiao Liu et al. "DARTS: DIFFERENTIABLE ARCHITECTURE SEARCH." In ICLR 2019. (CMU)

GNN

  • Peter W. Battaglia, et al. "Relational inductive biases, deep learning, and graph networks." arXiv: 1806.01261

NLP

  • Multi Agent :"MULTI-AGENT DUAL LEARNING" In ICLR 2019

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

61 stars

Watchers

3 watching

Forks

9 forks
Report repository

Releases

No releases published

Packages

No packages published