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...
| Hlavní autoři: | , |
|---|---|
| Médium: | Conference item |
| Vydáno: |
IEEE Explore
2017
|