Self-supervised learning of structural representations of visual objects
This thesis explores how a computer can learn the structure of visual objects in the absence of strong supervision using self-supervised learning. We demonstrate that we can learn structural representations of objects using an autoencoding framework with reconstruction as the key learning signal. We...
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| 格式: | Thesis |
| 語言: | English |
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2021
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