Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification

Top-k error is currently a popular performance measure on large scale image classification benchmarks such as ImageNet and Places. Despite its wide acceptance, our understanding of this metric is limited as most of the previous research is focused on its special case, the top-1 error. In this work,...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2018-07, Vol.40 (7), p.1533-1554
Main Authors: Lapin, Maksim, Hein, Matthias, Schiele, Bernt
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Benchmark testing
Calibration
Error analysis
Image classification
Loss measurement
Multiclass classification
multilabel classification
Optimization
SDCA optimization
Support vector machines
top-k calibration
top-k error
Training
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