Top-k Multiclass SVM
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@inproceedings{lapin2015nips,
title = {Top-k Multiclass {SVM}},
author = {Maksim Lapin and Matthias Hein and Bernt Schiele},
booktitle = {NIPS},
year = {2015}
}
Top-k SVM addresses the class ambiguity challenge
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- Ambiguous classes arise due to various reasons:
- certain images exhibit multi-label nature;
- in fine-grained classification it is by design;
- large scale problems come along with non-negligible class overlap as well as label noise.
- Top-k performance addresses this issue:
- one guess is too hard even for humans;
- allowing a few guesses is natural when the number of classes is large;
- top-k error is already reported routinely in popular benchmarks, such as the ImageNet challenge.
- In this work, we propose our Top-k Multiclass SVM, which
- generalizes the Multiclass SVM of Crammer and Singer;
- optimizes a tight convex upper bound on top-k error;
- is trained efficiently via Stochastic Dual Coordinate Ascent (SDCA);
- scales to large datasets such as ImageNet and Places;
- demonstrates consistent improvements in top-k accuracy.