Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning

Paper


Chen, Zhi, Huang, Zi, Li, Jingjing and Zhang, Zheng. 2021. "Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning." 32nd Australasian Database Conference (ADC 2021). Dunedin, New Zealand 29 Jan - 05 Feb 2021 Switzerland . Springer. https://doi.org/10.1007/978-3-030-69377-0_12
Paper/Presentation Title

Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning

Presentation TypePaper
AuthorsChen, Zhi, Huang, Zi, Li, Jingjing and Zhang, Zheng
Journal or Proceedings TitleProceedings of the 32nd Australasian Database Conference (ADC 2021)
Journal Citation12610, pp. 139-151
Number of Pages13
Year2021
PublisherSpringer
Place of PublicationSwitzerland
ISSN1611-3349
0302-9743
ISBN9783030693763
9783030693770
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-69377-0_12
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-3-030-69377-0_12
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-3-030-69377-0
Conference/Event32nd Australasian Database Conference (ADC 2021)
Event Details
32nd Australasian Database Conference (ADC 2021)
Parent
Australasian Database Conference
Delivery
In person
Event Date
29 Jan 2021 to end of 05 Feb 2021
Event Location
Dunedin, New Zealand
Rank
B
B
B
B
B
B
Abstract

Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. However, these types of models are prone to overfitting the seen classes, resulting in distribution overlap between the generated features of the seen and unseen classes. The overlapping region is filled with uncertainty as the model struggles to determine whether a test case from within the overlap is seen or unseen. Further, these generative methods suffer in scenarios with sparse training samples. The models struggle to learn the distribution of high dimensional visual features and, therefore, fail to capture the most discriminative inter-class features. To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes. To calibrate the uncertainty for seen classes, we calculate the entropy over the softmax probability distribution from a general classifier. With this approach, recognising the seen samples within the seen classes is relatively straightforward, and there is less risk that a seen sample will be misclassified into an unseen class in the overlapped region. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches.

KeywordsGeneralized zero shot learning; Image classification; Transfer learning; Triplet network
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

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SeriesLecture Notes in Computer Science
Byline AffiliationsUniversity of Queensland
University of Electronic Science and Technology of China, China
Harbin Institute of Technology, China
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