Loss Functions for Top-k Error: Analysis and Insights

CVPR 2016 Maksim LapinMatthias HeinBernt Schiele

In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right performance measure... (read more)

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