Bi-directional Distribution Alignment for Transductive Zero-Shot Learning

• URL: http://arxiv.org/abs/2303.08698v2
• Date: Sun, 19 Mar 2023 08:00:20 GMT
• Title: Bi-directional Distribution Alignment for Transductive Zero-Shot Learning
• Authors: Zhicai Wang, Yanbin Hao, Tingting Mu, Ouxiang Li, Shuo Wang, Xiangnan He
• Abstract summary: We propose a novel zero-shot learning model (TZSL) called Bi-VAEGAN. It largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. In benchmark evaluation, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings.
• Score: 48.80413182126543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is well-known that zero-shot learning (ZSL) can suffer severely from the problem of domain shift, where the true and learned data distributions for the unseen classes do not match. Although transductive ZSL (TZSL) attempts to improve this by allowing the use of unlabelled examples from the unseen classes, there is still a high level of distribution shift. We propose a novel TZSL model (named as Bi-VAEGAN), which largely improves the shift by a strengthened distribution alignment between the visual and auxiliary spaces. The key proposal of the model design includes (1) a bi-directional distribution alignment, (2) a simple but effective L_2-norm based feature normalization approach, and (3) a more sophisticated unseen class prior estimation approach. In benchmark evaluation using four datasets, Bi-VAEGAN achieves the new state of the arts under both the standard and generalized TZSL settings. Code could be found at https://github.com/Zhicaiwww/Bi-VAEGAN

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