Official implementation of the AAAI2023 Workshop paper : Model and Data Agreement for Learning with Noisy Labels
Learning with noisy labels is a vital topic for practical deep learning as models should be robust to noisy open-world datasets in the wild. The state-of-the-art noisy label learn- ing approach JoCoR fails when faced with a large ratio of noisy labels. Moreover, selecting small-loss samples can also cause error accumulation as once the noisy samples are mis- takenly selected as small-loss samples, they are more likely to be selected again. In this paper, we try to deal with error accumulation in noisy label learning from both model and data perspectives. We introduce mean point ensemble to utilize a more robust loss function and more information from unse- lected samples to reduce error accumulation from the model perspective. Furthermore, as the flip images have the same semantic meaning as the original images, we select small-loss samples according to the loss values of flip images instead of the original ones to reduce error accumulation from the data perspective. Extensive experiments on CIFAR-10, CIFAR- 100, and large-scale Clothing1M show that our method out- performs state-of-the-art noisy label learning methods with different levels of label noise. Our method can also be seam- lessly combined with other noisy label learning methods to further improve their performance and generalize well to other tasks.
Train with 80% label noise on CIFAR-100 with 4 GPUs
python train_cifar100.py