Web: http://arxiv.org/abs/2109.04898

Sept. 16, 2022, 1:15 a.m. | Wenbin Li, Ziyi, Wang, Xuesong Yang, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo

cs.CV updates on arXiv.org arxiv.org

Few-shot learning, especially few-shot image classification, has received
increasing attention and witnessed significant advances in recent years. Some
recent studies implicitly show that many generic techniques or ``tricks'', such
as data augmentation, pre-training, knowledge distillation, and
self-supervision, may greatly boost the performance of a few-shot learning
method. Moreover, different works may employ different software platforms,
backbone architectures and input image sizes, making fair comparisons difficult
and practitioners struggle with reproducibility. To address these situations,
we propose a comprehensive library for …

arxiv few-shot learning library

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