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meta-learning-study

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Deepest Season 6 Meta-Learning study papers plus alpha

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# Meta-Learning-Study
Deepest Season 6 Meta-Learning study papers plus alpha  
  
Those who are new to meta-learning, I recommend to start with reading these
+ Model-agnostic Meta-Learning for Fast Adaptation of Deep Networks
+ Prototypical Networks for Few-shot Learning
+ ICML 2019 Meta-Learning Tutorial [[link]](https://sites.google.com/view/icml19metalearning)
+ CS 330: Deep Multi-Task and Meta Learning [[link]](http://cs330.stanford.edu/)

## Optimization-based Meta-Learning
+ Model-agnostic Meta-Learning for Fast Adaptation of Deep Networks, (ICML 2017), [[link]](https://arxiv.org/abs/1703.03400)
+ Meta-Learning with Latent Embedding Optimization, (ICLR 2019), [[link]](https://arxiv.org/abs/1807.05960)
+ How to Train Your MAML, (ICLR 2019), [[link]](https://arxiv.org/abs/1810.09502)
+ Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML, (NeurIPs 2019 workshop)[[link]](https://arxiv.org/abs/1909.09157)
+ Meta-Learning with Implicit Gradients, (NIPS 2019), [[link]](https://arxiv.org/abs/1909.04630)
+ Meta-Learning with Warped Gradient Descent, (ICLR 2020), [[link]](https://openreview.net/forum?id=rkeiQlBFPB)

## Metric-Learning based Meta-Learning
+ Prototypical Networks for Few-shot Learning, (NIPS 2017), [[link]](https://arxiv.org/abs/1703.05175)
+ Learning to Compare: Relation Network for Few-Shot Learning, (CVPR 2018), [[link]](https://arxiv.org/abs/1711.06025)
+ TADAM: Task dependent adaptive metric for improved few-shot learning, (NIPS 2018)[[link]](https://arxiv.org/abs/1805.10123)
+ Infinite Mixture Prototypes for Few-Shot Learning, (ICML 2019), [[link]](https://arxiv.org/abs/1902.04552)

## Black-box adaptation based Meta-Learning
+ One-shot Learning with Memory-Augmented Neural Networks, (ArXiv 2016), [[link]](https://arxiv.org/abs/1605.06065)
+ Learning to learn by gradient descent by gradient descent, (NIPS 2016), [[link]](https://arxiv.org/abs/1606.04474)
+ A Simple Neural Attentive Meta-Learner, (ICLR 2018), [[link]](https://arxiv.org/abs/1707.03141)
+ Meta-Learning with Differentiable Convex Optimization, (CVPR 2019), [[link]](https://arxiv.org/abs/1904.03758)

## Bayesian Approaches
+ Towards a Neural Statistician, (ICLR 2017), [[link]](https://arxiv.org/abs/1606.02185)
+ Conditional Neural Processes, (ICML 2018), [[link]](https://arxiv.org/abs/1807.01613)
+ Probabilistic Model-Agnostic Meta-Learning, (NIPS 2018), [[link]](https://arxiv.org/abs/1806.02817)

## Generation
+ Few-Shot Adversarial Learning of Realistic Neural Talking Head Models, (ICCV 2019), [[link]](https://arxiv.org/abs/1905.08233)
+ Few-Shot Adaptive Gaze Estimation, (ICCV 2019), [[link]](https://arxiv.org/abs/1905.01941)
+ MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets, (AAAI 2020), [[link]](https://arxiv.org/abs/1911.08139)
+ MetaPix: Few-Shot Video Retargeting, (ICLR 2020), [[link]](https://openreview.net/forum?id=SJx1URNKwH)

## Unsupervised, Representation
+ Unsupervised Learning via Meta-Learning, (ICLR 2019), [[link]](https://arxiv.org/abs/1810.02334)
+ Meta-Learning Update Rules for Unsupervised Representation Learning, (ICLR 2019), [[link]](https://arxiv.org/abs/1804.00222)

## Realistic Setting
+ A Closer Look at Few-shot Classification, (ICLR 2019), [[link]](https://arxiv.org/abs/1904.04232)
+ Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples, (ICLR 2020 under review), [[link]](https://arxiv.org/abs/1903.03096)
+ Meta-Learning without Memorization, (ICLR2020), [[link]](https://arxiv.org/abs/1912.03820)

## Object Detection and Segmentation
+ CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning, (CVPR 2019), [[link]](https://arxiv.org/abs/1903.02351)
+ Few-shot Object Detection via Feature Reweighting, (ICCV 2019), [[link]](https://arxiv.org/abs/1812.01866)
+ Meta-Learning to Detect Rare Objects, (ICCV 2019), [[link]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf)

## Self-Supervised Learning
+ Boosting Few-Shot Visual Learning with Self-Supervision, (ICCV 2019), [[link]](https://arxiv.org/abs/1906.05186)
+ Charting the Right Manifold: Manifold Mixup for Few-shot Learning, (ArXiv 2019), [[link]](https://arxiv.org/abs/1907.12087)

## Before 2016

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