Near-Real Feature Generative Network for Generalized Zero-Shot Learning
Jingren Liu, Haoyue Bai, Haofeng Zhang, Li Liu · 2021
Due to the powerful feature synthesis ability, Generative Adversarial Networks (GAN) is well adapted to the Generalized Zero-Shot Learning (GZSL) task and has achieved great success. Most GAN models for GZSL usually employ random noise with normal distribution to synthesize unseen samples. However, the generated samples often have the same normal distribution as the input noise, which is unrealistic in most circumstances. Therefore, in this paper, we consider that the distribution of unseen classes should be follow that of seen classes and propose a near-real feature generative network (NereNet), which utilizes the most semantically similar seen samples to generate the noise for the unseen classes. Specifically, we first calculate the most similar seen classes for the unseen classes, and then train an encoder network to generate the corresponding noise, which is subsequently combined with the unseen classes attributes to generate unseen samples with GAN. Extensive experiments are conducted on four datasets, and the results demonstrate the effectiveness of our proposed method.