Mutual Conditional Probability for Self-Supervised Learning
Takumi Kobayashi (National Institute of Advanced Industrial Science and Technology)*
The 33rd British Machine Vision Conference
Abstract
Deep neural networks produce effective image feature representation through a supervised learning with plenty of annotation. To mitigate the label-hungry issue, self-supervised learning (SSL) works well for training the deep models without manual supervision. In SSL, pair-wise matching is widely applied to multi-view images via data augmentation techniques such as in a contrastive learning. In this work, we focus on a probabilistic distribution of the multi-view samples to effectively exploit the relationship among them which the pair-wise approaches hardly take into account. It leads to a novel loss based on mutual conditional probability through connecting SSL with mode seeking on the distribution. The method also exhibits connection to the other SSL methods. In the experiments on ImageNet classification tasks, the proposed method produces favorable performance in the framework of SSL.Video
Citation
@inproceedings{Kobayashi_2022_BMVC,
author = {Takumi Kobayashi},
title = {Mutual Conditional Probability for Self-Supervised Learning},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/1052.pdf}
}
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