Cross-domain self-supervised few-shot learning via multiple crops with teacher-student network

Author:

Wang Guangpeng,Wang YongxiongORCID,Zhang Jiapeng,Wang XiaomingORCID,Pan ZhiqunORCID

Funder

Natural Science Foundation of Shanghai Municipality

Publisher

Elsevier BV

Reference72 articles.

1. Pseudo-labeling and confirmation bias in deep semi-supervised learning;Arazo,2020

2. Unsupervised learning of visual features by contrasting cluster assignments;Caron;Adv. Neural Inf. Process. Syst.,2020

3. Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021. Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9650–9660.

4. A simple framework for contrastive learning of visual representations;Chen,2020

5. Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C.F., Huang, J.-B., 2019. A Closer Look at Few-shot Classification. In: International Conference on Learning Representations.

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