SAN: Sampling Adversarial Networks for Zero-Shot Learning

Chenwei Tang, Yangzhu Kuang, Jiancheng Lv, Jinglu Hu

研究成果: Conference contribution

抄録

In this paper, we propose a Sampling Adversarial Networks (SAN) framework to improve Zero-Shot Learning (ZSL) by mitigating the hubness and semantic gap problem. The SAN framework incorporates a sampling model and a discriminating model, and corresponds them to the minimax two-player game. Specifically, given the semantic embedding, the sampling model samples the visual features from the training set to approach the discriminator’s decision boundary. Then, the discriminator distinguishes the matching visual-semantic pairs from the sampled data. On the one hand, by the measurement of the matching degree of visual-semantic pairs and the adversarial training way, the visual-semantic embedding built by the proposed SAN decreases the intra-class distance and increases the inter-class separation. Then, the reduction of universal neighbours in the visual-semantic embedding subspace alleviates the hubness problem. On the other, the sampled rather than directly generated visual features maintain the same manifold as the real data, mitigating the semantic gap problem. Experiments show that the sampler and discriminator of the SAN framework outperform state-of-the-art methods both in conventional and generalized ZSL settings.

本文言語English
ホスト出版物のタイトルNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
編集者Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
出版社Springer Science and Business Media Deutschland GmbH
ページ626-638
ページ数13
ISBN(印刷版)9783030638320
DOI
出版ステータスPublished - 2020
イベント27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
継続期間: 2020 11 182020 11 22

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
12533 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference27th International Conference on Neural Information Processing, ICONIP 2020
国/地域Thailand
CityBangkok
Period20/11/1820/11/22

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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