Generalized Zero Shot Learning via Synthesis Pseudo Features

Chuanlong Li,Haibo Yang,Yatong Han,Xiang Li,Yunpeng Jia,Xiufen Ye

doi:10.1109/access.2019.2925093

Abstract

Compared with conventional zero-shot learning (ZSL), generalized ZSL (GZSL) is more challenging because the test instances may come from seen and unseen classes. The most existing GZSL methods learn a visual-semantic mapping function to bridge the knowledge transfer from seen to unseen classes by using semantic information and other labeled training data. However, these methods often suffer from severe performance degradation because they ignore similar structures between different classes. To solve these problems, we propose a GZSL method that transforms GZSL problems to conventional supervised learning ones by synthesizing pseudo features for unseen classes. This technique has two key aspects. The first one is the synthesis strategy; the proposed strategy directly synthesizes the pseudo features of unseen classes contrary to current synthesis-based methods, which synthesize pseudo instances. Our method regards the combination of N features of instances as the pseudo features. These N features belong to N different classes that are similar to unseen ones. This synthesis strategy is in line with the cognitive style of human beings. The second key aspect is that we preserve the similar structures between seen and unseen classes. Inspired by the center loss method, we assign each semantic vector as the center of deep features in the training stage. This way preserves the similar structures between the classes. Such preservation can be beneficial for improving classification accuracy. The experimental results on four benchmark datasets demonstrate that our model outperforms state-of-the-art methods for the GZSL. The source code is available at https://github.com/guizilaile23/SPF-GZSL.

Highlights

Supervised learning methods have achieved significant successes in many areas with sufficient labeled training data provided for each class [1], [2]
RELATED WORK we summarize the existing zero-shot learning (ZSL) methods into three strategies based on the strategy that they adopt. the strategies of most current generalized ZSL (GZSL) approaches generally include three types: mapping-based method, generate-based method, and synthesis-based method
FURTHER ANALYSIS There are two strategies in our method that are very effective in improving the accuracy of GZSL, we further analyzed the impact of these strategies through two experiments

Summary

Introduction

Supervised learning methods have achieved significant successes in many areas with sufficient labeled training data provided for each class [1], [2]. Certain classes only have a small quantity or even no training data, resulting in the failure of the conventional supervised method [3]. Unlike traditional supervised learning methods, zero-shot learning (ZSL) [4] aims to recognize instances in which. ZSL methods usually learn knowledge from training sets that belong to seen classes, wherein sufficient labeled instances are provided [4]–[7]. With the help of auxiliary information that contains descriptions of seen and unseen classes, ZSL methods can generate predictions for instances that belong to unseen classes despite that the seen and unseen classes are disjointed

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Conclusion