Delta-encoder: an effective sample synthesis method for few-shot object recognition

Computer Vision


Published on


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.

Please cite our work using the BibTeX below.

  author    = {Eli Schwartz and
               Leonid Karlinsky and
               Joseph Shtok and
               Sivan Harary and
               Mattias Marder and
               Rog{\'{e}}rio Schmidt Feris and
               Abhishek Kumar and
               Raja Giryes and
               Alexander M. Bronstein},
  title     = {Delta-encoder: an effective sample synthesis method for few-shot object
  journal   = {CoRR},
  volume    = {abs/1806.04734},
  year      = {2018},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1806.04734},
  timestamp = {Wed, 16 Oct 2019 14:14:57 +0200},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

Close Modal