Suppressing Mislabeled Data via Grouping and Self-Attention

• URL: http://arxiv.org/abs/2010.15603v1
• Date: Thu, 29 Oct 2020 13:54:16 GMT
• Title: Suppressing Mislabeled Data via Grouping and Self-Attention
• Authors: Xiaojiang Peng, Kai Wang, Zhaoyang Zeng, Qing Li, Jianfei Yang and Yu Qiao
• Abstract summary: Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. This paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM) It allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups.
• Score: 60.14212694011875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM), which allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups. Specifically, this plug-and-play AFM first leverages a \textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then uses a \textit{mixup} module with the attention weights to interpolate massive noisy-suppressed samples. The AFM has several appealing benefits for noise-robust deep learning. (i) It does not rely on any assumptions and extra clean subset. (ii) With massive interpolations, the ratio of useless samples is reduced dramatically compared to the original noisy ratio. (iii) \pxj{It jointly optimizes the interpolation weights with classifiers, suppressing the influence of mislabeled data via low attention weights. (iv) It partially inherits the vicinal risk minimization of mixup to alleviate over-fitting while improves it by sampling fewer feature-target vectors around mislabeled data from the mixup vicinal distribution.} Extensive experiments demonstrate that AFM yields state-of-the-art results on two challenging real-world noisy datasets: Food101N and Clothing1M. The code will be available at https://github.com/kaiwang960112/AFM.

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