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Easy—Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components

Few-shot classification aims at leveraging knowledge learned in a deep learning model, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair number of works in the field, each one introducing their own methodology. A frequent problem, though, is the use of suboptimally trained models as a first building block, leading to doubts about whether proposed approaches bring gains if applied to more sophisticated pretrained models. In this work, we propose a simple way to train such models, with the aim of reaching top performance on multiple standardized benchmarks in the field. This methodology offers a new baseline on which to propose (and fairly compare) new techniques or adapt existing ones.

@article{BenHuLafLioPasPatGri20227,
  author = {Yassir Bendou and Yuqing Hu and Raphael
Lafargue and Giulia Lioi and Bastien Pasdeloup and
Stéphane Pateux and Vincent Gripon},
  title = {Easy—Ensemble Augmented-Shot-Y-Shaped
Learning: State-of-the-Art Few-Shot Classification
with Simple Components},
  journal = {MDPI Journal of Imaging},
  year = {2022},
  volume = {8},
  number = {7},
  month = {July},
}