Enhanced VAEGAN: a zero-shot image classification method

Zero-shot learning (ZSL) aims to classify samples of unseen categories for which no training data is available. At present, the VAEGAN framework which combines Generative Adversarial Networks (GAN) with Variational Auto-Encoder (VAE) has achieved good performance in zero-shot image classification. Based on the VAEGAN, we propose a new zero-shot image classification method named Enhanced VAEGAN (E-VAEGAN). Firstly, we design a feature alignment module to align visual features and attribute features. Then, the aligned features are fused with the hidden layer features of the encoder to improve output features of the encoder. Secondly, the triplet loss is applied during the encoder training, which further increases the discriminability of features. Finally, the hidden layer features of the discriminator are input into a transform module and then fed back to the generator, which improves the quality of the generated fake samples. The originality of this paper is that we design a new E-VAEGAN which employs the feature alignment module, triplet loss and transform module to reduce the ambiguity between categories and make the generated fake features similar to the real features. Experiments show that our method outperforms the compared methods on five zero-shot learning benchmarks.