Learning Graph-Based Priors for Generalized Zero-Shot Learning
This work addresses the challenge of classifying both seen and unseen classes in GZSL, representing an incremental improvement over existing generative methods.
The paper tackled the problem of generalized zero-shot learning (GZSL) by incorporating a relation graph over labels to learn priors for a variational autoencoder, achieving improved performance on CUB and SUN benchmarks.
The task of zero-shot learning (ZSL) requires correctly predicting the label of samples from classes which were unseen at training time. This is achieved by leveraging side information about class labels, such as label attributes or word embeddings. Recently, attention has shifted to the more realistic task of generalized ZSL (GZSL) where test sets consist of seen and unseen samples. Recent approaches to GZSL have shown the value of generative models, which are used to generate samples from unseen classes. In this work, we incorporate an additional source of side information in the form of a relation graph over labels. We leverage this graph in order to learn a set of prior distributions, which encourage an aligned variational autoencoder (VAE) model to learn embeddings which respect the graph structure. Using this approach we are able to achieve improved performance on the CUB and SUN benchmarks over a strong baseline.