• Corpus ID: 219966219

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification

  title={Self-Supervised Prototypical Transfer Learning for Few-Shot Classification},
  author={Carlos Medina and Arnout Devos and Matthias Grossglauser},
Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduction in few-shot classification performance. Simultaneously, in settings with realistic domain shift, common transfer learning has been shown to outperform supervised meta-learning. Building on these insights and on advances in self-supervised learning, we propose a transfer… 

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