Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Tongzhou Wang, Phillip Isola
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:9929-9939, 2020.

Abstract

Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-wang20k, title = {Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere}, author = {Wang, Tongzhou and Isola, Phillip}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {9929--9939}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/wang20k/wang20k.pdf}, url = {https://proceedings.mlr.press/v119/wang20k.html}, abstract = {Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning.} }
Endnote
%0 Conference Paper %T Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere %A Tongzhou Wang %A Phillip Isola %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-wang20k %I PMLR %P 9929--9939 %U https://proceedings.mlr.press/v119/wang20k.html %V 119 %X Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning.
APA
Wang, T. & Isola, P.. (2020). Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:9929-9939 Available from https://proceedings.mlr.press/v119/wang20k.html.

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