Self-training For Few-shot Transfer Across Extreme Task DifferencesDownload PDF

28 Sep 2020 (modified: 25 Jan 2021)ICLR 2021 OralReaders: Everyone
  • Keywords: few-shot learning, self-training, cross-domain few-shot learning
  • Abstract: Most few-shot learning techniques are pre-trained on a large, labeled “base dataset”. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different “source” problem domain (e.g., ImageNet), which can be very different from the desired target task. Traditional few-shot and transfer learning techniques fail in the presence of such extreme differences between the source and target tasks. In this paper, we present a simple and effective solution to tackle this extreme domain gap: self-training a source domain representation on unlabeled data from the target domain. We show that this improves one-shot performance on the target domain by 2.9 points on average on the challenging BSCD-FSL benchmark consisting of datasets from multiple domains.
  • One-sentence Summary: Self-training a source domain classifier on unlabeled data from the target domain improves cross-domain few-shot transfer.
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