Variational Self-Supervised Contrastive Learning Using Beta Divergence

• URL: http://arxiv.org/abs/2312.00824v3
• Date: Wed, 8 May 2024 14:27:20 GMT
• Title: Variational Self-Supervised Contrastive Learning Using Beta Divergence
• Authors: Mehmet Can Yavuz, Berrin Yanikoglu,
• Abstract summary: We present a contrastive self-supervised learning method which is robust to data noise, grounded in the domain of variational methods. We demonstrate the effectiveness of the proposed method through rigorous experiments including linear evaluation and fine-tuning scenarios with multi-label datasets in the face understanding domain.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning a discriminative semantic space using unlabelled and noisy data remains unaddressed in a multi-label setting. We present a contrastive self-supervised learning method which is robust to data noise, grounded in the domain of variational methods. The method (VCL) utilizes variational contrastive learning with beta-divergence to learn robustly from unlabelled datasets, including uncurated and noisy datasets. We demonstrate the effectiveness of the proposed method through rigorous experiments including linear evaluation and fine-tuning scenarios with multi-label datasets in the face understanding domain. In almost all tested scenarios, VCL surpasses the performance of state-of-the-art self-supervised methods, achieving a noteworthy increase in accuracy.

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