Multi-task Self-Supervised Visual Learning

Carl Doersch; Andrew Zisserman

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arXiv · 1708.07860

Multi-task Self-Supervised Visual Learning

Carl Doersch·Andrew Zisserman

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Abstract

We investigate methods for combining multiple self-supervised tasks--i.e., supervised tasks where data can be collected without manual labeling--in order to train a single visual representation. First, we provide an apples-to-apples comparison of four different self-supervised tasks using the very deep ResNet-101 architecture. We then combine tasks to jointly train a network. We also explore lasso regularization to encourage the network to factorize the information in its representation, and methods for "harmonizing" network inputs in order to learn a more unified representation. We evaluate all methods on ImageNet classification, PASCAL VOC detection, and NYU depth prediction. Our results show that deeper networks work better, and that combining tasks--even via a naive multi-head architecture--always improves performance. Our best joint network nearly matches the PASCAL performance of a model pre-trained on ImageNet classification, and matches the ImageNet network on NYU depth prediction.

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BibTeX RIS

Carl Doersch, Andrew Zisserman. 2017-08-25. Multi-task Self-Supervised Visual Learning. https://arxiv.org/abs/1708.07860

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