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Conference Papers Year : 2020

PAC-Bayesian Contrastive Unsupervised Representation Learning

Abstract

Contrastive unsupervised representation learning (CURL) is the state-of-the-art technique to learn representations (as a set of features) from unlabelled data. While CURL has collected several empirical successes recently, theoretical understanding of its performance was still missing. In a recent work, Arora et al. (2019) provide the first generalisation bounds for CURL, relying on a Rademacher complexity. We extend their framework to the flexible PAC-Bayes setting, allowing to deal with the non-iid setting. We present PAC-Bayesian generalisation bounds for CURL, which are then used to derive a new representation learning algorithm. Numerical experiments on real-life datasets illustrate that our algorithm achieves competitive accuracy, and yields generalisation bounds with non-vacuous values.
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Dates and versions

hal-02401282 , version 1 (09-12-2019)

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Kento Nozawa, Pascal Germain, Benjamin Guedj. PAC-Bayesian Contrastive Unsupervised Representation Learning. UAI 2020 - Conference on Uncertainty in Artificial Intelligence, Aug 2020, Toronto, Canada. ⟨hal-02401282⟩
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