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https://github.com/e-271/awesome-few-shot-learning

A curated list of resources about few-shot and one-shot learning
https://github.com/e-271/awesome-few-shot-learning

List: awesome-few-shot-learning

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A curated list of resources about few-shot and one-shot learning

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# awesome-few-shot-learning :boom: [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

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



Inspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision/).



Also check out [awesome-zero-shot-learning](https://github.com/chichilicious/awesome-zero-shot-learning).



## Meta-learning

### Model optimzation

* [Unsupervised Meta-Learning for Few-Shot Image and Video Classification [Khodadadeh et al. 2018]](https://arxiv.org/pdf/1811.11819.pdf)

* [A Simple Neural Attentive Meta-Learner [Mishra et al. 2018]](https://arxiv.org/pdf/1707.03141.pdf)

* [Neural Optimizer Search with Reinforcement Learning [Bello 2017]](https://ai.googleblog.com/2018/03/using-machine-learning-to-discover.html)

* [Optimization as a Model for Few-Shot Learning [Ravi, Larochelle 2017]](https://openreview.net/pdf?id=rJY0-Kcll)

* [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [Finn et al. 2017]](https://arxiv.org/pdf/1703.03400.pdf)



### Metric learning

* [TADAM: Task dependent adaptive metric for improved few-shot learning [Oreshkin et al. 2019]](https://arxiv.org/pdf/1805.10123.pdf)

* [Learning to Compare: Relation Network for Few-Shot Learning [Sung et al. 2018]](https://arxiv.org/pdf/1711.06025.pdf)

* [Meta-Learning for Semi-Supervised Few-Shot Classification [Triantafillou et al. 2018]](https://ai.google/research/pubs/pub46640)

* [Prototypical Networks for Few-shot Learning [Snell et al. 2017]](https://arxiv.org/pdf/1703.05175.pdf)

* [Matching Networks for One Shot Learning [Vinyals et al. 2017]](https://arxiv.org/pdf/1606.04080.pdf)

* [Transfer of View-Manifold Learning to Similarity Perception of Novel Objects [Lin et al. 2017]](https://arxiv.org/pdf/1704.00033.pdf)

* [Generative Adversarial Residual Pairwise Networks for One Shot Learning [Mehrota & Dukkipatti 2017]](https://arxiv.org/abs/1703.08033)

* [Siamese Neural Networks for One-shot Image Recognition [Koch et al. 2015]](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)



### Data augmentation

* [Data Augmentation Generative Adversarial Networks [Antoniou et al. 2018]](https://arxiv.org/pdf/1711.04340.pdf)

* [Low-Shot Learning from Imaginary Data [Wang et al. 2018]](https://arxiv.org/pdf/1801.05401.pdf)

* [Low-shot Visual Recognition by Shrinking and Hallucinating Features [Hariharan, Girshick 2017]](https://arxiv.org/pdf/1606.02819.pdf)



### Attention mechanism

* [Dynamic Few-Shot Visual Learning without Forgetting [Gidaris & Komodakis 2018]](https://arxiv.org/pdf/1804.09458.pdf)

* [Meta Networks [Munkhdalai & Yu 2017]](https://arxiv.org/pdf/1703.00837.pdf)

* [One-shot Learning with Memory-Augmented Neural Networks [Santoro 2016]](https://arxiv.org/pdf/1605.06065.pdf)



## Other approaches

* [Adaptive Cross-Modal Few-Shot Learning [Xing et al. 2019]](https://arxiv.org/pdf/1902.07104v1.pdf)

* [Few-Shot Learning with Graph Neural Networks [Garcia & Bruna 2018]](https://arxiv.org/pdf/1711.04043.pdf)

* [Low-Shot Learning with Imprinted Weights [Qi et al. 2018]](https://arxiv.org/pdf/1712.07136.pdf)

* [Few-Shot Image Recognition by Predicting Parameters from Activations [Qiao et al. 2017]](https://arxiv.org/pdf/1706.03466.pdf)

* [Active One-shot Learning [Woodward et al. 2017]](https://arxiv.org/pdf/1702.06559.pdf)

* [Towarads a Neural Statistician [Edwards & Storkey 2017]](https://arxiv.org/pdf/1606.02185.pdf)