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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
Last synced: 3 days ago
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Meta-learning
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Model optimzation
- Unsupervised Meta-Learning for Few-Shot Image and Video Classification [Khodadadeh et al. 2018
- A Simple Neural Attentive Meta-Learner [Mishra et al. 2018
- 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
- A Simple Neural Attentive Meta-Learner [Mishra et al. 2018
- Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks [Finn et al. 2017
- Unsupervised Meta-Learning for Few-Shot Image and Video Classification [Khodadadeh et al. 2018
- Neural Optimizer Search with Reinforcement Learning [Bello 2017
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Metric learning
- 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
- Learning to Compare: Relation Network for Few-Shot Learning [Sung et al. 2018
- Prototypical Networks for Few-shot Learning [Snell et al. 2017
- Matching Networks for One Shot Learning [Vinyals et al. 2017
- TADAM: Task dependent adaptive metric for improved few-shot learning [Oreshkin et al. 2019
- Transfer of View-Manifold Learning to Similarity Perception of Novel Objects [Lin et al. 2017
- Meta-Learning for Semi-Supervised Few-Shot Classification [Triantafillou et al. 2018
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Data augmentation
- 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
- Data Augmentation Generative Adversarial Networks [Antoniou et al. 2018
- Low-shot Visual Recognition by Shrinking and Hallucinating Features [Hariharan, Girshick 2017
- Low-Shot Learning from Imaginary Data [Wang et al. 2018
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Attention mechanism
- Dynamic Few-Shot Visual Learning without Forgetting [Gidaris & Komodakis 2018
- Meta Networks [Munkhdalai & Yu 2017
- One-shot Learning with Memory-Augmented Neural Networks [Santoro 2016
- Dynamic Few-Shot Visual Learning without Forgetting [Gidaris & Komodakis 2018
- One-shot Learning with Memory-Augmented Neural Networks [Santoro 2016
- Meta Networks [Munkhdalai & Yu 2017
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Other approaches
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Attention mechanism
- 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
- Few-Shot Learning with Graph Neural Networks [Garcia & Bruna 2018
- Few-Shot Image Recognition by Predicting Parameters from Activations [Qiao et al. 2017
- Adaptive Cross-Modal Few-Shot Learning [Xing et al. 2019
- Low-Shot Learning with Imprinted Weights [Qi et al. 2018
- Active One-shot Learning [Woodward et al. 2017
- Towarads a Neural Statistician [Edwards & Storkey 2017
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