SigCLR: Sigmoid Contrastive Learning of Visual Representations

Ömer Veysel Çağatan

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arXiv · 2410.17427

SigCLR: Sigmoid Contrastive Learning of Visual Representations

Ömer Veysel Çağatan

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Abstract

We propose SigCLR: Sigmoid Contrastive Learning of Visual Representations. SigCLR utilizes the logistic loss that only operates on pairs and does not require a global view as in the cross-entropy loss used in SimCLR. We show that logistic loss shows competitive performance on CIFAR-10, CIFAR-100, and Tiny-IN compared to other established SSL objectives. Our findings verify the importance of learnable bias as in the case of SigLUP, however, it requires a fixed temperature as in the SimCLR to excel. Overall, SigCLR is a promising replacement for the SimCLR which is ubiquitous and has shown tremendous success in various domains.

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Ömer Veysel Çağatan. 2024-10-22. SigCLR: Sigmoid Contrastive Learning of Visual Representations. https://arxiv.org/abs/2410.17427

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