Mitigating Generation Shifts for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information to recognize seen and unseen samples, where unseen classes are not observable during training. It is natural to derive generative models and hallucinate training samples for unseen classes based on the knowledge learned from the seen samples. However, most of these models suffer from the generation shifts, where the synthesized samples may drift from the real distribution of unseen data. In this paper, we propose a novel generative flow framework that consists of multiple conditional affine coupling layers for learning unseen data generation. In particular, we identify three potential problems that trigger the generation shifts, i.e., semantic inconsistency, variance collapse, and structure disorder and address them respectively. First, to reinforce the correlations between the generated samples and their corresponding attributes, we explicitly embed the semantic information into the transformations in each coupling layer. Second, to recover the intrinsic variance of the real unseen features, we introduce a visual perturbation strategy to diversify the generated data and hereby help adjust the decision boundary of the classifiers. Third, a relative positioning strategy is proposed to revise the attribute embeddings, guiding them to fully preserve the inter-class geometric structure and further avoid structure disorder in the semantic space. Experimental results demonstrate that GSMFlow achieves the state-of-the-art performance on GZSL.