Unseen image generating domain-free networks for generalized zero-shot learning

In generalized zero-shot learning (GZSL), it is imperative to solve the bias problem due to extreme data imbalance between seen and unseen classes, i.e., unseen classes are misclassified as seen classes. We alleviate the bias problem by generating synthetic images of unseen classes. The most challenging part is that existing GAN methods are only focused on producing authentic seen images, so realistic unseen images cannot be generated. Specifically, we propose a novel zero-shot generative adversarial network (ZSGAN) which learns the relationship between images and attributes shared by seen and unseen classes. Unlike existing works that generate synthetic features of unseen classes, we can generate more generalizable realistic unseen images. For instance, generated unseen images can be used for zero-shot detection, segmentation, and image translation since images have spatial information. We also propose domain-free networks (DFN) that can effectively distinguish seen and unseen domains for input images. We evaluate our approaches on three challenging GZSL datasets, including CUB, FLO, and AWA2. We outperform the state-of-the-art methods and also empirically verify that our proposed method is a network-agnostic approach, i.e., the generated unseen images can improve performance regardless of the neural network type.