  Zero-shot learning (ZSL) addresses the unseen class recognition problem by
leveraging semantic information to transfer knowledge from seen classes to
unseen classes. Generative models synthesize the unseen visual features and
convert ZSL into a classical supervised learning problem. These generative
models are trained using the seen classes and are expected to implicitly
transfer the knowledge from seen to unseen classes. However, their performance
is stymied by overfitting, which leads to substandard performance on
Generalized Zero-Shot learning (GZSL). To address this concern, we propose the
novel LsrGAN, a generative model that Leverages the Semantic Relationship
between seen and unseen categories and explicitly performs knowledge transfer
by incorporating a novel Semantic Regularized Loss (SR-Loss). The SR-loss
guides the LsrGAN to generate visual features that mirror the semantic
relationships between seen and unseen classes. Experiments on seven benchmark
datasets, including the challenging Wikipedia text-based CUB and NABirds
splits, and Attribute-based AWA, CUB, and SUN, demonstrates the superiority of
the LsrGAN compared to previous state-of-the-art approaches under both ZSL and
GZSL. Code is available at https: // github. com/ Maunil/ LsrGAN
