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Densely Annotated Photorealistic Virtual Dataset Generation for Abnormal Event Detection Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Soft Pseudo-labeling Semi-Supervised Learning Applied to Fine-Grained Visual Classification

Authors : Daniele Mugnai, Federico Pernici, Francesco Turchini, Alberto Del Bimbo

Published in: Pattern Recognition. ICPR International Workshops and Challenges

Publisher: Springer International Publishing

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Abstract

Pseudo-labeling is a simple and well known strategy in Semi-Supervised Learning with neural networks. The method is equivalent to entropy minimization as the overlap of class probability distribution can be reduced minimizing the entropy for unlabeled data. In this paper we review the relationship between the two methods and evaluate their performance on Fine-Grained Visual Classification datasets. We include also the recent released iNaturalist-Aves that is specifically designed for Semi-Supervised Learning. Experimental results show that although in some cases supervised learning may still have better performance than the semi-supervised methods, Semi Supervised Learning shows effective results. Specifically, we observed that entropy-minimization slightly outperforms a recent proposed method based on pseudo-labeling.

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previous chapter ADNet: Temporal Anomaly Detection in Surveillance Videos
next chapter Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection
Footnotes
1
The Semi-Supervised iNaturalist-Aves Dataset: https://​github.​com/​cvl-umass/​semi-inat-2020.
 
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Metadata
Title
Soft Pseudo-labeling Semi-Supervised Learning Applied to Fine-Grained Visual Classification
Authors
Daniele Mugnai
Federico Pernici
Francesco Turchini
Alberto Del Bimbo
Copyright Year
2021
Book
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68798-4

Electronic ISBN: 978-3-030-68799-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-68799-1_8

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