Loss Functions for Top-k Error: Analysis and Insights
Paper | Long version | 
libsdca
@inproceedings{lapin2016cvpr,
title = {Loss Functions for Top-k Error: Analysis and Insights},
author = {Maksim Lapin and Matthias Hein and Bernt Schiele},
booktitle = {CVPR},
year = {2016}
}
Empirical and Theoretical Evaluation of 10 Loss Functions on 11+ Datasets
- We study top-k error optimization on a diverse range of learning tasks.
- We consider 6 existing methods and propose 4 novel loss functions for minimizing the top-k error.
- We develop an optimization scheme based on SDCA, which can be used with the softmax loss.
- All methods are evaluated empirically and, whenever possible, in terms of classification calibration.
- We discover that the softmax loss and the proposed smooth top-1 SVM are competitive in all top-k errors.
- Further small improvements can be obtained with the new top-k losses.
- This is a follow-up on the Top-k Multiclass SVM.