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Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning

Most studies in zero-shot learning model the relationship, in the form of a classifier or mapping, between features from images of seen classes and their attributes. Therefore, the degree of a model's generalization ability for recognizing unseen images is highly constrained by that of image features and attributes. In this paper, we discuss two questions about generalization that are seldom discussed. Are image features trained with samples of seen classes expressive enough to capture the discriminative information for both seen and unseen classes? Is the relationship learned from seen image features and attributes sufficiently generalized to recognize unseen classes. To answer these two questions, we propose a model to learn discriminative and generalizable representations from image features under an auto-encoder framework. The discriminative latent features are learned through a group-wise disentanglement over feature groups with a hierarchical structure. On popular benchmark data sets, a significant improvement over state-of-the-art methods in tasks of typical and generalized zero-shot learning verifies the generalization ability of latent features for recognizing unseen images.

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