Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning

TL;DR

This paper introduces a novel zero-shot learning model that uses an information bottleneck constraint and bidirectional embedding to improve generalization to unseen classes, outperforming existing methods.

Contribution

It proposes a unified latent space with an information bottleneck constraint for better visual-semantic alignment in ZSL, addressing hubness and bias issues.

Findings

Outperforms state-of-the-art ZSL methods on benchmark datasets.

Effectively reduces seen-unseen bias in generative ZSL models.

Extends to transductive ZSL with label noise handling.

Abstract

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Though many ZSL methods rely on a direct mapping between the visual and the semantic space, the calibration deviation and hubness problem limit the generalization capability to unseen classes. Recently emerged generative ZSL methods generate unseen image features to transform ZSL into a supervised classification problem. However, most generative models still suffer from the seen-unseen bias problem as only seen data is used for training. To address these issues, we propose a novel bidirectional embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Since the embedding from high-dimensional visual features comprise much non-semantic information, the alignment of visual and semantic in latent space would inevitably been deviated. Therefore, we introduce information bottleneck (IB) constraint to ZSL for the first time to preserve essential attribute information during the mapping. Specifically, we utilize the uncertainty estimation and the wake-sleep procedure to alleviate the feature noises and improve model abstraction capability. In addition, our method can be easily extended to transductive ZSL setting by generating labels for unseen images. We then introduce a robust loss to solve this label noise problem. Extensive experimental results show that our method outperforms the state-of-the-art methods in different ZSL settings on most benchmark datasets. The code will be available at https://github.com/osierboy/IBZSL.