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Generative Generalized Zero-Shot Learning Based on Auxiliary-Features

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Neural Information Processing (ICONIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1791))

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Abstract

Although generalized zero-shot learning (GZSL) has achieved success in recognizing images of unseen classes, most previous studies focused on feature projection from one domain to another, neglecting the importance of semantic descriptions. In this paper, we propose auxiliary-features via GAN(Af-GAN) to deal with the semantic loss problem. Auxiliary-features contain both real features of seen classes and instructive-features mapped by attributes. For the seen classes, we deploy the auxiliary-features to train the generator and regularize the synthesized samples to be close to auxiliary-features. For the unseen classes, we take the instructive-feature mapped by attributes to synthesize unseen class samples for training the final classifier. We construct the constraint between real visual features and instructive-features, and reduce the dependence on the class attributes. Considering that features synthesized from a set of similar attributes overlap each other in visual space, we combine Cosine similarity and Euclidean distance to constrain the distribution of synthesized features. Our method outperforms state-of-the-art methods on four benchmark datasets and also surpasses prior work by a large margin in generalized zero-shot learning.

This research was supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (20XNA031).The computer resources were provided by Public Computing Cloud Platform of Renmin University of China.

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Author information

Authors and Affiliations

  1. Key Lab of Data Engineering and Knowledge Engineering, Beijing, China

    Gang Yang

  2. School of Information, Renmin University of China, Beijing, China

    Weimin Sun & Gang Yang

Authors
  1. Weimin Sun
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  2. Gang Yang
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Corresponding author

Correspondence to Gang Yang .

Editor information

Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Sun, W., Yang, G. (2023). Generative Generalized Zero-Shot Learning Based on Auxiliary-Features. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_44

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  • DOI: https://doi.org/10.1007/978-981-99-1639-9_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1638-2

  • Online ISBN: 978-981-99-1639-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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