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OLÉ, Orthogonal Low-rank Embedding, A Novel Approach for Deep Metric Learning| old_uid | 16029 |
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| title | OLÉ, Orthogonal Low-rank Embedding, A Novel Approach for Deep Metric Learning |
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| start_date | 2018/06/14 |
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| schedule | 14h-15h |
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| online | no |
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| summary | Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are common tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin of the learned deep representations. To simultaneously achieve these two goals, different solutions have been proposed in the literature, such as the pairwise or triplet losses. However, such solutions carry the extra task of selecting pairs or triplets, and the extra computational burden of computing and learning for many combinations of them. In this talk we present a plug-and-play loss term for deep networks that explicitly reduces intra-class variance and enforces inter-class margin simultaneously, in a simple geometric manner. For each class, the deep features are collapsed into a learned linear subspace, or union of them, and inter-class subspaces are pushed to be as orthogonal as possible. Our proposed Orthogonal Low-rank Embedding does not require carefully crafting pairs or triplets of samples for training, and works standalone as a classification loss. Because of the improved margin between features of different classes, the resulting deep networks generalize better, are more discriminative and more robust. This is a joint work with José Lezama, Qiang Qiu and Guillermo Sapiro |
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| responsibles | Almansa, Delon |
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