{"id":13444452,"url":"https://github.com/e-271/awesome-few-shot-learning","last_synced_at":"2026-01-23T05:14:51.882Z","repository":{"id":90862271,"uuid":"162732272","full_name":"e-271/awesome-few-shot-learning","owner":"e-271","description":"A curated list of resources about few-shot and one-shot 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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)\nResources to learn about few-shot and one-shot learning. \n\nInspired by [awesome-computer-vision](https://github.com/jbhuang0604/awesome-computer-vision/).\n\nAlso check out [awesome-zero-shot-learning](https://github.com/chichilicious/awesome-zero-shot-learning).\n\n## Meta-learning\n### Model optimzation\n* [Unsupervised Meta-Learning for Few-Shot Image and Video Classification [Khodadadeh et al. 2018]](https://arxiv.org/pdf/1811.11819.pdf)\n* [A Simple Neural Attentive Meta-Learner [Mishra et al. 2018]](https://arxiv.org/pdf/1707.03141.pdf)\n* [Neural Optimizer Search with Reinforcement Learning [Bello 2017]](https://ai.googleblog.com/2018/03/using-machine-learning-to-discover.html)\n* [Optimization as a Model for Few-Shot Learning [Ravi, Larochelle 2017]](https://openreview.net/pdf?id=rJY0-Kcll)\n* [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [Finn et al. 2017]](https://arxiv.org/pdf/1703.03400.pdf)\n\n### Metric learning\n* [TADAM: Task dependent adaptive metric for improved few-shot learning [Oreshkin et al. 2019]](https://arxiv.org/pdf/1805.10123.pdf)\n* [Learning to Compare: Relation Network for Few-Shot Learning [Sung et al. 2018]](https://arxiv.org/pdf/1711.06025.pdf)\n* [Meta-Learning for Semi-Supervised Few-Shot Classification [Triantafillou et al. 2018]](https://ai.google/research/pubs/pub46640)\n* [Prototypical Networks for Few-shot Learning [Snell et al. 2017]](https://arxiv.org/pdf/1703.05175.pdf)\n* [Matching Networks for One Shot Learning [Vinyals et al. 2017]](https://arxiv.org/pdf/1606.04080.pdf)\n* [Transfer of View-Manifold Learning to Similarity Perception of Novel Objects [Lin et al. 2017]](https://arxiv.org/pdf/1704.00033.pdf)\n* [Generative Adversarial Residual Pairwise Networks for One Shot Learning [Mehrota \u0026 Dukkipatti 2017]](https://arxiv.org/abs/1703.08033)\n* [Siamese Neural Networks for One-shot Image Recognition [Koch et al. 2015]](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)\n\n### Data augmentation\n* [Data Augmentation Generative Adversarial Networks [Antoniou et al. 2018]](https://arxiv.org/pdf/1711.04340.pdf)\n* [Low-Shot Learning from Imaginary Data [Wang et al. 2018]](https://arxiv.org/pdf/1801.05401.pdf)\n* [Low-shot Visual Recognition by Shrinking and Hallucinating Features [Hariharan, Girshick 2017]](https://arxiv.org/pdf/1606.02819.pdf)\n\n### Attention mechanism\n* [Dynamic Few-Shot Visual Learning without Forgetting [Gidaris \u0026 Komodakis 2018]](https://arxiv.org/pdf/1804.09458.pdf)\n* [Meta Networks [Munkhdalai \u0026 Yu 2017]](https://arxiv.org/pdf/1703.00837.pdf)\n* [One-shot Learning with Memory-Augmented Neural Networks [Santoro 2016]](https://arxiv.org/pdf/1605.06065.pdf)\n\n## Other approaches\n* [Adaptive Cross-Modal Few-Shot Learning [Xing et al. 2019]](https://arxiv.org/pdf/1902.07104v1.pdf)\n* [Few-Shot Learning with Graph Neural Networks [Garcia \u0026 Bruna 2018]](https://arxiv.org/pdf/1711.04043.pdf)\n* [Low-Shot Learning with Imprinted Weights [Qi et al. 2018]](https://arxiv.org/pdf/1712.07136.pdf)\n* [Few-Shot Image Recognition by Predicting Parameters from Activations [Qiao et al. 2017]](https://arxiv.org/pdf/1706.03466.pdf)\n* [Active One-shot Learning [Woodward et al. 2017]](https://arxiv.org/pdf/1702.06559.pdf)\n* [Towarads a Neural Statistician [Edwards \u0026 Storkey 2017]](https://arxiv.org/pdf/1606.02185.pdf)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fe-271%2Fawesome-few-shot-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fe-271%2Fawesome-few-shot-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fe-271%2Fawesome-few-shot-learning/lists"}