Meta-learning
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]
- 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]
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]
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]
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]
Other approaches
- 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]
