S3: Supervised Self-supervised Learning under Label Noise

22 Nov 2021  ·  Chen Feng, Georgios Tzimiropoulos, Ioannis Patras ·

Despite the large progress in supervised learning with Neural Networks, there are significant challenges in obtaining high-quality, large-scale and accurately labeled datasets. In this context, in this paper we address the problem of classification in the presence of label noise and more specifically, both close-set and open-set label noise, that is when the true label of a sample may, or may not belong to the set of the given labels. In the heart of our method is a sample selection mechanism that relies on the consistency between the annotated label of a sample and the distribution of the labels in its neighborhood in the feature space; a relabeling mechanism that relies on the confidence of the classifier across subsequent iterations; and a training strategy that trains the encoder both with a self-consistency loss and the classifier-encoder with the cross-entropy loss on the selected samples alone. Without bells and whistles, such as co-training so as to reduce the self-confirmation bias, and with robustness with respect to settings of its few hyper-parameters, our method significantly surpasses previous methods on both CIFAR10/CIFAR100 with artificial noise and real-world noisy datasets such as WebVision and ANIMAL-10N.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Learning with noisy labels ANIMAL S3 Accuracy 88.5 # 1
Image Classification Clothing1M S3 Accuracy 74.91% # 6
Image Classification mini WebVision 1.0 S3 (ResNet-18) Top-1 Accuracy 80.12 # 6
Top-5 Accuracy 92.80 # 7
ImageNet Top-1 Accuracy 74.84 # 14
ImageNet Top-5 Accuracy 91.26 # 17

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