# Transfer feature generating networks with semantic classes structure for zero-shot learning

## TL;DR

This paper introduces TFGNSCS, a transfer feature generating network that leverages semantic class structures and transfer learning to improve zero-shot and generalized zero-shot learning performance.

## Contribution

The paper proposes a novel transfer feature generating network that incorporates semantic class structures and transfer learning to enhance ZSL and GZSL accuracy.

## Key findings

- Outperforms state-of-the-art methods on four datasets
- Effectively models semantic class relationships
- Improves feature generation for unseen classes

## Abstract

Feature generating networks face to the most important question, which is the fitting difference (inconsistence) of the distribution between the generated feature and the real data. This inconsistence further influence the performance of the networks model, because training samples from seen classes is disjointed with testing samples from unseen classes in zero-shot learning (ZSL). In generalization zero-shot learning (GZSL), testing samples come from not only seen classes but also unseen classes for closer to the practical situation. Therefore, most of feature generating networks difficultly obtain satisfactory performance for the challenging GZSL by adversarial learning the distribution of semantic classes. To alleviate the negative influence of this inconsistence for ZSL and GZSL, transfer feature generating networks with semantic classes structure (TFGNSCS) is proposed to construct networks model for improving the performance of ZSL and GZSL. TFGNSCS can not only consider the semantic structure relationship between seen and unseen classes, but also learn the difference of generating features by transferring classification model information from seen to unseen classes in networks. The proposed method can integrate the transfer loss, the classification loss and the Wasserstein distance loss to generate enough CNN features, on which softmax classifiers are trained for ZSL and GZSL. Experiments demonstrate that the performance of TFGNSCS outperforms that of the state of the arts on four challenging datasets, which are CUB,FLO,SUN, AWA in GZSL.