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- Title
基于判别器反馈的零样本图像分类方法.
- Authors
范宇飞; 丁博; 何勇军
- Abstract
Zero-shot learning (ZSL) strives to classify unseen categories for which no data is available during training. At present, among generative methods, zero-shot learning based on joint generative model VAEGAN is a research hotspot. On this basis, we propose a zero-shot image classification method based on Discriminator Feedback VAEGAN (DF-VAEGAN). This method introduces a feedback module in the discriminator part, which can improve the overall performance of the model in the training stage. In the feature generation stage, it can be combined with the generator to jointly improve the quality of feature generation. Finally, the classifier is trained through high quality synthetic features to improve classification accuracy. The method also reconstructs attribute features through the decoder and uses a cycle consistency loss to ensure semantic consistency of the generated feature. Experiments on ZSL and generalized zero-shot learning (GZSL) show that our method outperforms existing methods on five classical datasets, effectively enhancing the quality of feature synthesis and reducing the goal of between categories in the zero-shot image classification task.
- Subjects
IMAGE recognition (Computer vision); GENERATIVE adversarial networks
- Publication
Journal of Harbin University of Science & Technology, 2023, Vol 28, Issue 1, p46
- ISSN
1007-2683
- Publication type
Article
- DOI
10.15938/j.jhust.2023.01.006