Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images
This addresses the problem of recognizing graphic symbols like traffic signs with minimal training data, but it appears incremental as it builds on existing one-shot learning approaches.
The paper tackles open-set graphic symbol recognition using one-shot classification with prototypical images, proposing a variational prototyping-encoder (VPE) that learns image translation to prototypical images as a meta-task, resulting in favorable performance against competing methods in experiments.
In daily life, graphic symbols, such as traffic signs and brand logos, are ubiquitously utilized around us due to its intuitive expression beyond language boundary. We tackle an open-set graphic symbol recognition problem by one-shot classification with prototypical images as a single training example for each novel class. We take an approach to learn a generalizable embedding space for novel tasks. We propose a new approach called variational prototyping-encoder (VPE) that learns the image translation task from real-world input images to their corresponding prototypical images as a meta-task. As a result, VPE learns image similarity as well as prototypical concepts which differs from widely used metric learning based approaches. Our experiments with diverse datasets demonstrate that the proposed VPE performs favorably against competing metric learning based one-shot methods. Also, our qualitative analyses show that our meta-task induces an effective embedding space suitable for unseen data representation.