Hybrid Discriminative-Generative Training via Contrastive Learning

• URL: http://arxiv.org/abs/2007.09070v2
• Date: Mon, 10 Aug 2020 07:34:31 GMT
• Title: Hybrid Discriminative-Generative Training via Contrastive Learning
• Authors: Hao Liu, Pieter Abbeel
• Abstract summary: We show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning. We show our specific choice of approximation of the energy-based loss outperforms the existing practice in terms of classification accuracy of WideResNet on CIFAR-10 and CIFAR-100.
• Score: 96.56164427726203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive learning and supervised learning have both seen significant progress and success. However, thus far they have largely been treated as two separate objectives, brought together only by having a shared neural network. In this paper we show that through the perspective of hybrid discriminative-generative training of energy-based models we can make a direct connection between contrastive learning and supervised learning. Beyond presenting this unified view, we show our specific choice of approximation of the energy-based loss outperforms the existing practice in terms of classification accuracy of WideResNet on CIFAR-10 and CIFAR-100. It also leads to improved performance on robustness, out-of-distribution detection, and calibration.

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