Generative Model-driven Structure Aligning Discriminative Embeddings for Transductive Zero-shot Learning

Understand

Zero-shot Learning (ZSL) is a transfer learning technique which aims at transferring knowledge from seen classes to unseen classes.

• This knowledge transfer is possible because of underlying semantic space which is common to seen and unseen classes.
• Most existing approaches learn a projection function using labelled seen class data which maps visual data to semantic data.
• In this work, we propose a shallow but effective neural network-based model for learning such a projection function which aligns the visual and semantic data in the latent space while simultaneously making the latent space embeddings discriminative.