Energy-Based Contrastive Learning of Visual Representations
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Contrastive learning is a method of learning visual representations by training Deep Neural Networks (DNNs) to increase the similarity between representations of positive pairs and reduce the similarity between representations of negative pairs. However, contrastive methods usually require large datasets with significant number of negative pairs per iteration to achieve reasonable performance on downstream tasks. To address this problem, here we propose Energy-Based Contrastive Learning (EBCLR) that combines contrastive learning with Energy-Based Models (EBMs) and can be theoretically interpreted as learning the joint distribution of positive pairs. Using a novel variant of Stochastic Gradient Langevin Dynamics (SGLD) to accelerate the training of EBCLR, we show that EBCLR is far more sample-efficient than previous self-supervised learning methods. Specifically, EBCLR shows from X4 up to X20 acceleration compared to SimCLR and MoCo v2 in terms of training epochs. Furthermore, in contrast to SimCLR, EBCLR achieves nearly the same performance with 254 negative pairs (batch size 128) and 30 negative pairs (batch size 16) per positive pair, demonstrating the robustness of EBCLR to small number of negative pairs.
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