Multi-task self-supervised visual learning

Multi-task self-supervised visual learning

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 dee...

書誌詳細
主要な著者:	Doersch, C, Zisserman, A
フォーマット:	Conference item
出版事項:	IEEE Explore 2017

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