Zero-shot learning via a specific rank-controlled semantic autoencoder

Abstract

Existing embedding zero-shot learning models usually learn a projection function from the visual feature space to the semantic embedding space, e.g. attribute space or word vector space. However, the projection learned based on seen samples may not generalize well to unseen classes, which is known as the projection domain shift problem in ZSL. To address this issue, we propose a method named Low-rank Semantic Autoencoder (LSA) to consider the low-rank structure of seen samples to maintain the sparse feature of reconstruction error, which can further improve zero-shot learning capability. Moreover, to obtain a more robust projection for unseen classes, we propose a Specific Rank-controlled Semantic Autoencoder (SRSA) to accurately control of the projection’s rank. Extensive experiments on six benchmarks demonstrate the superiority of the proposed models over most existing embedding ZSL models under the standard zero-shot setting and the more realistic generalized zero-shot setting.