awesome-few-shot-learning 💥

Resources to learn about few-shot and one-shot learning.

Meta-learning

Unsupervised Meta-Learning for Few-Shot Image and Video Classification [Khodadadeh et al. 2018]
A Simple Neural Attentive Meta-Learner [Mishra et al. 2018]
Neural Optimizer Search with Reinforcement Learning [Bello 2017]
Optimization as a Model for Few-Shot Learning [Ravi, Larochelle 2017]
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [Finn et al. 2017]

TADAM: Task dependent adaptive metric for improved few-shot learning [Oreshkin et al. 2019]
Learning to Compare: Relation Network for Few-Shot Learning [Sung et al. 2018]
Meta-Learning for Semi-Supervised Few-Shot Classification [Triantafillou et al. 2018]
Prototypical Networks for Few-shot Learning [Snell et al. 2017]
Matching Networks for One Shot Learning [Vinyals et al. 2017]
Transfer of View-Manifold Learning to Similarity Perception of Novel Objects [Lin et al. 2017]
Generative Adversarial Residual Pairwise Networks for One Shot Learning [Mehrota & Dukkipatti 2017]
Siamese Neural Networks for One-shot Image Recognition [Koch et al. 2015]

Data Augmentation Generative Adversarial Networks [Antoniou et al. 2018]
Low-Shot Learning from Imaginary Data [Wang et al. 2018]
Low-shot Visual Recognition by Shrinking and Hallucinating Features [Hariharan, Girshick 2017]

Adaptive Cross-Modal Few-Shot Learning [Xing et al. 2019]
Few-Shot Learning with Graph Neural Networks [Garcia & Bruna 2018]
Low-Shot Learning with Imprinted Weights [Qi et al. 2018]
Few-Shot Image Recognition by Predicting Parameters from Activations [Qiao et al. 2017]
Active One-shot Learning [Woodward et al. 2017]
Towarads a Neural Statistician [Edwards & Storkey 2017]