Zero-shot Image Classification Method Based on Discriminator Feedback - FAN Yufei; DING Bo; HE Yongjun (Journal of Harbin University of Science and Technology)
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.