Web: http://arxiv.org/abs/2206.08150

June 17, 2022, 1:13 a.m. | Xueliang Wang, Jianyu Cai, Shuiwang Ji, Houqiang Li, Feng Wu, Jie Wang

cs.CV updates on arXiv.org arxiv.org

Few-shot classification aims to learn a model that can generalize well to new
tasks when only a few labeled samples are available. To make use of unlabeled
data that are more abundantly available in real applications, Ren et al.
\shortcite{ren2018meta} propose a semi-supervised few-shot classification
method that assigns an appropriate label to each unlabeled sample by a manually
defined metric. However, the manually defined metric fails to capture the
intrinsic property in data. In this paper, we propose a
\textbf{S}elf-\textbf{A}daptive …

arxiv augmentation classification cv semi-supervised

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