Exploring Simple Siamese Representation Learning

2021/06/01 by Xinlei Chen, Kaiming He · 299 citations
Computer Science · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #Multimodal Machine Learning Applications

paper · doi:10.1109/cvpr46437.2021.01549

openalex publication_date 2021/06/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn meaningful representations even using none of the following: (i) negative sample pairs, (ii) large batches, (iii) momentum encoders. Our experiments show that collapsing solutions do exist for the loss and structure, but a stop-gradient operation plays an essential role in preventing collapsing. We provide a hypothesis on the implication of stop-gradient, and further show proof-of-concept experiments verifying it. Our "SimSiam" method achieves competitive results on ImageNet and downstream tasks. We hope this simple baseline will motivate people to rethink the roles of Siamese architectures for unsupervised representation learning. Code is made available.1

Citations

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• Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• Contrastive Multiview Coding
• Representation Learning with Contrastive Predictive Coding

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