On the Similarities of Embeddings in Contrastive Learning

Chungpa Lee, Sehee Lim, Kibok Lee, Jy-yong Sohn

TL;DR: We present a unified framework for understanding contrastive learning, which is based on analyzing the cosine similarity between embeddings of positive and negative pairs.

Abstract: Contrastive learning (CL) operates on a simple yet effective principle: embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. Although various forms of contrastive loss have been proposed and analyzed from different perspectives, prior works lack a comprehensive framework that systematically explains a broad class of these objectives. In this paper, we present a unified framework for understanding CL, which is based on analyzing the cosine similarity between embeddings of positive and negative pairs. In full-batch settings, we show that perfect alignment of positive pairs is unattainable when similarities of negative pairs fall below a certain threshold, and that this misalignment can be alleviated by incorporating within-view negative pairs. In mini-batch settings, we demonstrate that smaller batch sizes incur stronger separation among negative pairs within batches, which leads to higher variance in similarities of negative pairs. To address this limitation of mini-batch CL, we introduce an auxiliary loss term that reduces the variance of similarities of negative pairs in CL. Empirical results demonstrate that incorporating the proposed loss consistently improves the performance of CL methods in small-batch training.

Lay Summary: Contrastive learning (CL) operates on a simple yet effective principle: embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. Although various forms of contrastive loss have been proposed and analyzed from different perspectives, prior works lack a comprehensive framework that systematically explains a broad class of these objectives. In this paper, we present a unified framework for understanding CL, which is based on analyzing the cosine similarity between embeddings of positive and negative pairs. In full-batch settings, we show that perfect alignment of positive pairs is unattainable when similarities of negative pairs fall below a certain threshold, and that this misalignment can be alleviated by incorporating within-view negative pairs. In mini-batch settings, we demonstrate that smaller batch sizes incur stronger separation among negative pairs within batches, which leads to higher variance in similarities of negative pairs. To address this limitation of mini-batch CL, we introduce an auxiliary loss term that reduces the variance of similarities of negative pairs in CL. Empirical results demonstrate that incorporating the proposed loss consistently improves the performance of CL methods in small-batch training.

Link To Code: https://github.com/leechungpa/embedding-similarity-cl

Primary Area: Theory

Keywords: contrastive learning, representation learning, embedding, similarity, negative pair, positive pair, variance

Submission Number: 2413