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Self-Supervised Representation Learning as Multimodal Variational Inference

2022-03-22 03:17:15
Hiroki Nakamura, Masashi Okada, Tadahiro Taniguchi

Abstract

This paper proposes a probabilistic extension of SimSiam, a recent self-supervised learning (SSL) method. SimSiam trains a model by maximizing the similarity between image representations of different augmented views of the same image. Although uncertainty-aware machine learning has been getting general like deep variational inference, SimSiam and other SSL are insufficiently uncertainty-aware, which could lead to limitations on its potential. The proposed extension is to make SimSiam uncertainty-aware based on variational inference. Our main contributions are twofold: Firstly, we clarify the theoretical relationship between non-contrastive SSL and multimodal variational inference. Secondly, we introduce a novel SSL called variational inference SimSiam (VI-SimSiam), which incorporates the uncertainty by involving spherical posterior distributions. Our experiment shows that VI-SimSiam outperforms SimSiam in classification tasks in ImageNette and ImageWoof by successfully estimating the representation uncertainty.

Abstract (translated)

URL

https://arxiv.org/abs/2203.11437

PDF

https://arxiv.org/pdf/2203.11437.pdf


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