# Multi-view Contrastive Learning for Online Knowledge Distillation

Source: [Multi-view Contrastive Learning for Online Knowledge Distillation](<https://www.sota2.com/research/paper/68fa69b91e209131cea23c2c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:07:38.441Z

Authors: Chuanguang Yang, Zhulin An, Yongjun Xu

Published: 2020-06-07T09:11:28.000Z

## Abstract

Previous Online Knowledge Distillation (OKD) often carries out mutually exchanging probability distributions, but neglects the useful representational knowledge. We therefore propose Multi-view Contrastive Learning (MCL) for OKD to implicitly capture correlations of feature embeddings encoded by multiple peer networks, which provide various views for understanding the input data instances. Benefiting from MCL, we can learn a more discriminative representation space for classification than previous OKD methods. Experimental results on image classification demonstrate that our MCL-OKD outperforms other state-of-the-art OKD methods by large margins without sacrificing additional inference cost. Codes are available at https://github.com/winycg/MCL-OKD.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.04093>)

[PDF](<https://arxiv.org/pdf/2006.04093.pdf>)