Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Supervised Prototypical Transfer Learning for Few-Shot
  Classification   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 learning approach which
constructs a metric embedding that clusters unlabeled prototypical samples and
their augmentations closely together. This pre-trained embedding is a starting
point for few-shot classification by summarizing class clusters and
fine-tuning. We demonstrate that our self-supervised prototypical transfer
learning approach ProtoTransfer outperforms state-of-the-art unsupervised
meta-learning methods on few-shot tasks from the mini-ImageNet dataset. In
few-shot experiments with domain shift, our approach even has comparable
performance to supervised methods, but requires orders of magnitude fewer
labels.

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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: math