An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise

• URL: http://arxiv.org/abs/2501.01130v1
• Date: Thu, 02 Jan 2025 08:12:23 GMT
• Title: An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise
• Authors: Jingyi Cui, Yi-Ge Zhang, Hengyu Liu, Yisen Wang,
• Abstract summary: We propose a unified theoretical framework for robust losses under the pairwise contrastive paradigm. We derive a general robust condition for arbitrary contrastive losses, which serves as a criterion to verify the theoretical robustness of a supervised contrastive loss against label noise. We show that our theory is an inclusive framework that provides explanations to prior robust techniques.
• Score: 20.696895070598643
• License:
• Abstract: Learning from noisy labels is a critical challenge in machine learning, with vast implications for numerous real-world scenarios. While supervised contrastive learning has recently emerged as a powerful tool for navigating label noise, many existing solutions remain heuristic, often devoid of a systematic theoretical foundation for crafting robust supervised contrastive losses. To address the gap, in this paper, we propose a unified theoretical framework for robust losses under the pairwise contrastive paradigm. In particular, we for the first time derive a general robust condition for arbitrary contrastive losses, which serves as a criterion to verify the theoretical robustness of a supervised contrastive loss against label noise. The theory indicates that the popular InfoNCE loss is in fact non-robust, and accordingly inspires us to develop a robust version of InfoNCE, termed Symmetric InfoNCE (SymNCE). Moreover, we highlight that our theory is an inclusive framework that provides explanations to prior robust techniques such as nearest-neighbor (NN) sample selection and robust contrastive loss. Validation experiments on benchmark datasets demonstrate the superiority of SymNCE against label noise.

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