Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning

Kento Nozawa, Issei Sato

Keywords: representation learning, self-supervised representation learning

TL;DR: We give a theoretical analysis to explain a benefit of large negative samples in self-supervised contrastive learning.

Abstract: Instance discriminative self-supervised representation learning has been attracted attention thanks to its unsupervised nature and informative feature representation for downstream tasks. In practice, it commonly uses a larger number of negative samples than the number of supervised classes. However, there is an inconsistency in the existing analysis; theoretically, a large number of negative samples degrade classification performance on a downstream supervised task, while empirically, they improve the performance. We provide a novel framework to analyze this empirical result regarding negative samples using the coupon collector's problem. Our bound can implicitly incorporate the supervised loss of the downstream task in the self-supervised loss by increasing the number of negative samples. We confirm that our proposed analysis holds on real-world benchmark datasets.

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Code: https://github.com/nzw0301/Understanding-Negative-Samples

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/understanding-negative-samples-in-instance/code)