Zero-Shot Learning with Many Classes by High-Rank Deep Embedding Networks

Abstract

Zero-shot learning (ZSL) is a recently emerging research topic which aims to build classification models for unseen classes with knowledge from auxiliary seen classes. Though many ZSL works have shown promising results on small-scale datasets by utilizing a bilinear compatibility function, the ZSL performance on large-scale datasets with many classes (say, ImageNet) is still unsatisfactory. We argue that the bilinear compatibility function is a low-rank approximation of the true compatibility function such that it is not expressive enough especially when there are a large number of classes because of the rank limitation. To address this issue, we propose a novel approach, termed as High-rank Deep Embedding Networks (GREEN), for ZSL with many classes. In particular, we propose a feature-dependent mixture of softmaxes as the image-class compatibility function, which is a simple extension of the bilinear compatibility function, but yields much better results. It utilizes a mixture of non-linear transformations with feature-dependent latent variables to approximate the true function in a high-rank way, which makes GREEN more expressive. Experiments on several datasets including ImageNet demonstrate GREEN significantly outperforms the state-of-the-art approaches.