few-shot_learning

some classic metric-based few-shot learning methods based on pytorch 1.x

Requirements

• Python 3.6 or above
• PyTorch 1.4 or above
• Torchvision 0.5 or above

Datasets

CV

• miniImagenet
• Omniglot

NLP

• FewRel

Methods

• Siamese-Networks
• Matching-Networks
• Prototypical-Networks
• Relation-Networks (LearningToCompare)

Reference

Tristan Deleu, Tobias Würfl, Mandana Samiei, Joseph Paul Cohen, and Yoshua Bengio. Torchmeta: A Meta-Learning library for PyTorch, 2019 [ArXiv]

Koch G, Zemel R, Salakhutdinov R. Siamese neural networks for one-shot image recognition[C]//ICML deep learning workshop. 2015, 2.

Vinyals O, Blundell C, Lillicrap T, et al. Matching networks for one shot learning[J]. arXiv preprint arXiv:1606.04080, 2016.

Snell J, Swersky K, Zemel R S. Prototypical networks for few-shot learning[J]. arXiv preprint arXiv:1703.05175, 2017.

Sung F, Yang Y, Zhang L, et al. Learning to compare: Relation network for few-shot learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 1199-1208.