Fine-Grained Classification with Noisy Labels

• URL: http://arxiv.org/abs/2303.02404v1
• Date: Sat, 4 Mar 2023 12:32:45 GMT
• Title: Fine-Grained Classification with Noisy Labels
• Authors: Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Chenhui Guo, Yilong Yin
• Abstract summary: Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. We investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG) We propose a novel framework called noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.
• Score: 31.128588235268126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning with noisy labels (LNL) aims to ensure model generalization given a label-corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine-grained datasets (LNL-FG), which is more practical and challenging as large inter-class ambiguities among fine-grained classes cause more noisy labels. We empirically show that existing methods that work well for LNL fail to achieve satisfying performance for LNL-FG, arising the practical need of effective solutions for LNL-FG. To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation. Specifically, we design a noise-tolerated supervised contrastive learning loss that incorporates a weight-aware mechanism for noisy label correction and selectively updating momentum queue lists. By this mechanism, we mitigate the effects of noisy anchors and avoid inserting noisy labels into the momentum-updated queue. Besides, to avoid manually-defined augmentation strategies in contrastive learning, we propose an efficient stochastic module that samples feature embeddings from a generated distribution, which can also enhance the representation ability of deep models. SNSCL is general and compatible with prevailing robust LNL strategies to improve their performance for LNL-FG. Extensive experiments demonstrate the effectiveness of SNSCL.

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