Auto‐encode the synthesis pseudo features for generalized zero‐shot learning

Abstract

Abstract Zero‐shot learning (ZSL) is to identify target categories without labeled data, in which semantic information is used to transfer knowledge from some seen categories. In the existing Generalized Zero‐Shot Learning (GZSL) methods, domains shift problem always appeared during generating feature stage. In order to solve this problem, a new method to Auto‐Encode the Synthesis Pseudo Features for the GZSL task (AESPF‐GZSL) is proposed in this manuscript. Specifically, the AESPF‐GZSL method trains the generated features under the semantic auto‐encoder framework and exploits attention mechanism to train the generated features again. Then, the generated features are input to the classifier. The proposed method is performed on three benchmark data sets referred as to AWA, CUB and SUN. The experimental results show that the proposed method achieves the state‐of‐the art classifier accuracy both in ZSL and GZSL settings. In ZSL setting, the classification accuracy of our method is superior to the compared algorithms, improved by 0.40% in AWA and 0.30% in SUN, respectively. And in GZSL setting, the classification accuracy of the method is superior to the comparison algorithm 0.41% in Harmonic mean on AWA, and 1.01%, 0.62%, and 1.05% in training data set, testing data set, and harmonic average on SUN.