# Discriminative Embedding Autoencoder with a Regressor Feedback for Zero-Shot Learning

## TL;DR

This paper introduces a discriminative autoencoder with regressor feedback for zero-shot learning, enhancing the discriminative features and semantic generalization to recognize unseen classes more effectively.

## Contribution

It proposes a novel autoencoder model with regressor feedback that improves discriminative feature learning and generalization in zero-shot learning tasks.

## Key findings

- Outperforms state-of-the-art models on four benchmark datasets.
- Achieves significant improvements in generalized zero-shot learning.
- Effectively learns discriminative features for object recognition.

## Abstract

Zero-shot learning (ZSL) aims to recognize the novel object categories using the semantic representation of categories, and the key idea is to explore the knowledge of how the novel class is semantically related to the familiar classes. Some typical models are to learn the proper embedding between the image feature space and the semantic space, whilst it is important to learn discriminative features and comprise the coarse-to-fine image feature and semantic information. In this paper, we propose a discriminative embedding autoencoder with a regressor feedback model for ZSL. The encoder learns a mapping from the image feature space to the discriminative embedding space, which regulates both inter-class and intra-class distances between the learned features by a margin, making the learned features be discriminative for object recognition. The regressor feedback learns to map the reconstructed samples back to the the discriminative embedding and the semantic embedding, assisting the decoder to improve the quality of the samples and provide a generalization to the unseen classes. The proposed model is validated extensively on four benchmark datasets: SUN, CUB, AWA1, AWA2, the experiment results show that our proposed model outperforms the state-of-the-art models, and especially in the generalized zero-shot learning (GZSL), significant improvements are achieved.