Beyond Normal Distribution: More Factual Feature Generation Network for Generalized Zero-Shot Learning

Due to the prosperous development of generative models, research works have achieved great success on the generalized zero-shot learning (GZSL) task. In most generative methods of GZSL, researchers try to utilize attributes and normally distributed noise to generate visual features, which ignores whether the normal distribution can perfectly represent all categories. Therefore, in this article, we exploit variational auto-encoders (VAE) and visual features to generate image-level noise that can preserve class-level characteristics in more detail and propose a mechanism called more factual generative network (MFGN) to achieve more authentic generative process. In other words, it is to transfer the seen feature distribution to the unseen domains and regulate the knowledge to correct the generation of unseen samples. Extensive experiments are conducted on four popular datasets and the results demonstrate the effectiveness of the proposed work.