Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective method for few-shot (and one-shot) object recognition. Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier. The proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class. The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case. Upon acceptance code will be made available.
@article{arxiv.1806.04734,
title = {Delta-encoder: an effective sample synthesis method for few-shot object recognition},
author = {Eli Schwartz and Leonid Karlinsky and Joseph Shtok and Sivan Harary and Mattias Marder and Rogerio Feris and Abhishek Kumar and Raja Giryes and Alex M. Bronstein},
journal= {arXiv preprint arXiv:1806.04734},
year = {2018}
}