Self-supervised learning of geometrically stable features through probabilistic introspection
Self-supervision can dramatically cut back the amount of manually-labeled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching...
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| Formato: | Conference item |
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Institute for Electrical and Electronics Engineers
2018
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