SAN: Sampling Adversarial Networks for Zero-Shot Learning

Chenwei Tang, Yangzhu Kuang, Jiancheng Lv*, Jinglu Hu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020, Proceedings
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030638320
Publication statusPublished - 2020
Event27th International Conference on Neural Information Processing, ICONIP 2020 - Bangkok, Thailand
Duration: 2020 Nov 182020 Nov 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12533 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th International Conference on Neural Information Processing, ICONIP 2020


  • Hubness problem
  • Sampling Adversarial Networks
  • Semantic gap
  • Zero-Shot Learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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