Аннотация
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 us 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 non-vacuous generalisation bounds.
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