A Message Passing Perspective on Learning Dynamics of Contrastive Learning

by   Yifei Wang, et al.

In recent years, contrastive learning achieves impressive results on self-supervised visual representation learning, but there still lacks a rigorous understanding of its learning dynamics. In this paper, we show that if we cast a contrastive objective equivalently into the feature space, then its learning dynamics admits an interpretable form. Specifically, we show that its gradient descent corresponds to a specific message passing scheme on the corresponding augmentation graph. Based on this perspective, we theoretically characterize how contrastive learning gradually learns discriminative features with the alignment update and the uniformity update. Meanwhile, this perspective also establishes an intriguing connection between contrastive learning and Message Passing Graph Neural Networks (MP-GNNs). This connection not only provides a unified understanding of many techniques independently developed in each community, but also enables us to borrow techniques from MP-GNNs to design new contrastive learning variants, such as graph attention, graph rewiring, jumpy knowledge techniques, etc. We believe that our message passing perspective not only provides a new theoretical understanding of contrastive learning dynamics, but also bridges the two seemingly independent areas together, which could inspire more interleaving studies to benefit from each other. The code is available at https://github.com/PKU-ML/Message-Passing-Contrastive-Learning.


Message passing all the way up

The message passing framework is the foundation of the immense success e...

ReFactorGNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective

Factorisation-based Models (FMs), such as DistMult, have enjoyed endurin...

Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework

Albeit having gained significant progress lately, large-scale graph repr...

Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages

Nowadays, Graph Neural Networks (GNNs) following the Message Passing par...

Collaboration-Aware Graph Convolutional Networks for Recommendation Systems

By virtue of the message-passing that implicitly injects collaborative e...

Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism

Recently, a variety of methods under the name of non-contrastive learnin...

GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

Graph is powerful for representing various types of real-world data. The...

Please sign up or login with your details

Forgot password? Click here to reset