Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks

• URL: http://arxiv.org/abs/2409.18685v1
• Date: Fri, 27 Sep 2024 12:19:41 GMT
• Title: Understanding the Benefits of SimCLR Pre-Training in Two-Layer Convolutional Neural Networks
• Authors: Han Zhang, Yuan Cao,
• Abstract summary: SimCLR is one of the most popular contrastive learning methods for vision tasks. We consider training a two-layer convolutional neural network (CNN) to learn a toy image data model. We show that, under certain conditions on the number of labeled data, SimCLR pre-training combined with supervised fine-tuning achieves almost optimal test loss.
• Score: 10.55004012983524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: SimCLR is one of the most popular contrastive learning methods for vision tasks. It pre-trains deep neural networks based on a large amount of unlabeled data by teaching the model to distinguish between positive and negative pairs of augmented images. It is believed that SimCLR can pre-train a deep neural network to learn efficient representations that can lead to a better performance of future supervised fine-tuning. Despite its effectiveness, our theoretical understanding of the underlying mechanisms of SimCLR is still limited. In this paper, we theoretically introduce a case study of the SimCLR method. Specifically, we consider training a two-layer convolutional neural network (CNN) to learn a toy image data model. We show that, under certain conditions on the number of labeled data, SimCLR pre-training combined with supervised fine-tuning achieves almost optimal test loss. Notably, the label complexity for SimCLR pre-training is far less demanding compared to direct training on supervised data. Our analysis sheds light on the benefits of SimCLR in learning with fewer labels.

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