Smooth loss functions for deep top-k classification
The top-$k$ error is a common measure of performance in machine learning and computer vision. In practice, top-$k$ classification is typically performed with deep neural networks trained with the cross-entropy loss. Theoretical results indeed suggest that cross-entropy is an optimal learning objecti...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
OpenReview
2018
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