# Semantically Aligned Bias Reducing Zero Shot Learning

## TL;DR

This paper introduces SABR, a novel zero shot learning approach that simultaneously addresses hubness and bias issues by learning a semantic-preserving latent space and employing bias reduction techniques, leading to significant performance improvements.

## Contribution

SABR is the first ZSL method to jointly tackle hubness and bias problems using a unified framework with semantic-preserving latent space and bias reduction strategies.

## Key findings

- Outperforms state-of-the-art by 1.5-9% in conventional ZSL
- Achieves 2-14% improvement in generalized ZSL
- Effective in both inductive and transductive settings

## Abstract

Zero shot learning (ZSL) aims to recognize unseen classes by exploiting semantic relationships between seen and unseen classes. Two major problems faced by ZSL algorithms are the hubness problem and the bias towards the seen classes. Existing ZSL methods focus on only one of these problems in the conventional and generalized ZSL setting. In this work, we propose a novel approach, Semantically Aligned Bias Reducing (SABR) ZSL, which focuses on solving both the problems. It overcomes the hubness problem by learning a latent space that preserves the semantic relationship between the labels while encoding the discriminating information about the classes. Further, we also propose ways to reduce the bias of the seen classes through a simple cross-validation process in the inductive setting and a novel weak transfer constraint in the transductive setting. Extensive experiments on three benchmark datasets suggest that the proposed model significantly outperforms existing state-of-the-art algorithms by ~1.5-9% in the conventional ZSL setting and by ~2-14% in the generalized ZSL for both the inductive and transductive settings.