Semantic-aware auto-encoders for self-supervised representation learning
The resurgence of unsupervised learning can be attributed to the remarkable progress of self-supervised learning, which includes generative $(\mathcal{G})$ and discriminative $(\mathcal{D})$ models. In computer vision, the mainstream self-supervised learning algorithms are $\mathcal{D}$ models. Howe...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2022
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