# FewShotPapers

**Repository Path**: marenan_admin/FewShotPapers

## Basic Information

- **Project Name**: FewShotPapers
- **Description**: This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 6
- **Forks**: 1
- **Created**: 2020-05-29
- **Last Updated**: 2025-08-25

## Categories & Tags

**Categories**: research

**Tags**: None

## README

# Few-Shot Papers

This repository contains few-shot learning (FSL) papers mentioned in our FSL survey published in ACM Computing Surveys (JCR Q1, CORE A*). 

We will update this paper list to include new FSL papers periodically.

## Citation 

Please cite our paper if you find it helpful.

```
@article{wang2020generalizing,
  title={Generalizing from a few examples: A survey on few-shot learning},
  author={Wang, Yaqing, Yao, Quanming, James T. Kwok, and Lionel M. Ni},
  journal={ACM Computing Surveys},
  year={2020}
}
```


## Content
1. [Survey](#Survey)
2. [Data](#Data)
3. [Model](#Model)
    1. [Multitask Learning](#Multitask-Learning)
    1. [Embedding Learning](#Embedding-Learning)
    1. [Learning with External Memory](#Learning-with-External-Memory)
    1. [Generative Modeling](#Generative-Modeling)
4. [Algorithm](#Algorithm)
    1. [Refining Existing Parameters](#Refining-Existing-Parameters)
    1. [Refining Meta-learned Parameters](#Refining-Meta-learned-Parameters)
    1. [Learning Search Steps](#Learning-Search-Steps)
5. [Applications](#Applications)
    1. [Computer Vision](#Computer-Vision)
    1. [Robotics](#Robotics)
    1. [Natural Language Processing](#Natural-Language-Processing)
    1. [Acoustic Signal Processing](#Acoustic-Signal-Processing)
    1. [Others](#others)
6. [Theories](##Theories)

## [Survey](#content)
1. **Generalizing from a few examples: A survey on few-shot learning,** CSUR, 2020
*Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni.*
[paper](https://arxiv.org/abs/1904.05046)

## [Data](#content)
1. **Learning from one example through shared densities on transforms,** in CVPR, 2000.
*E. G. Miller, N. E. Matsakis, and P. A. Viola.*
[paper](https://people.cs.umass.edu/~elm/papers/Miller_congealing.pdf)

1. **Domain-adaptive discriminative one-shot learning of gestures,** in ECCV, 2014.
*T. Pfister, J. Charles, and A. Zisserman.*
[paper](https://www.robots.ox.ac.uk/~vgg/publications/2014/Pfister14/pfister14.pdf)

1. **One-shot learning of scene locations via feature trajectory transfer,** in CVPR, 2016.
*R. Kwitt, S. Hegenbart, and M. Niethammer.* 
[paper](http://openaccess.thecvf.com/content_cvpr_2016/papers/Kwitt_One-Shot_Learning_of_CVPR_2016_paper.pdf)

1. **Low-shot visual recognition by shrinking and hallucinating features,** in ICCV, 2017.
*B. Hariharan and R. Girshick.*
[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Hariharan_Low-Shot_Visual_Recognition_ICCV_2017_paper.pdf)

1. **Improving one-shot learning through fusing side information,** arXiv preprint, 2017.
*Y.H.Tsai and R.Salakhutdinov.*
[paper](https://lld-workshop.github.io/2017/papers/LLD_2017_paper_31.pdf)

1. **Fast parameter adaptation for few-shot image captioning and visual question answering,** in ACM MM, 2018.
*X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu.* 
[paper](https://xuanyidong.com/resources/papers/ACM-MM-18-FPAIT.pdf)

1. **Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning,** in CVPR, 2018.
*Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, and Y. Yang.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Exploit_the_Unknown_CVPR_2018_paper.pdf)

1. **Low-shot learning with large-scale diffusion,** in CVPR, 2018.
*M. Douze, A. Szlam, B. Hariharan, and H. Jégou.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Douze_Low-Shot_Learning_With_CVPR_2018_paper.pdf)

1. **Diverse few-shot text classification with multiple metrics,** in NAACL-HLT, 2018.
*M. Yu, X. Guo, J. Yi, S. Chang, S. Potdar, Y. Cheng, G. Tesauro, H. Wang, and B. Zhou.* 
[paper](https://www.aclweb.org/anthology/N18-1109.pdf)

1. **Delta-encoder: An effective sample synthesis method for few-shot object recognition,** in NeurIPS, 2018.
*E. Schwartz, L. Karlinsky, J. Shtok, S. Harary, M. Marder, A. Kumar, R. Feris, R. Giryes, and A. Bronstein.*
[paper](https://papers.nips.cc/paper/7549-delta-encoder-an-effective-sample-synthesis-method-for-few-shot-object-recognition.pdf)

1. **Low-shot learning via covariance-preserving adversarial augmentation networks,** in NeurIPS, 2018.
*H. Gao, Z. Shou, A. Zareian, H. Zhang, and S. Chang.*
[paper](https://papers.nips.cc/paper/7376-low-shot-learning-via-covariance-preserving-adversarial-augmentation-networks.pdf)

1. **AutoAugment: Learning augmentation policies from data,** in CVPR, 2019.
*E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le.*
[paper](http://openaccess.thecvf.com/content_CVPR_2019/papers/Cubuk_AutoAugment_Learning_Augmentation_Strategies_From_Data_CVPR_2019_paper.pdf)

1. **EDA: Easy data augmentation techniques for boosting performance on text classification tasks,** in EMNLP and IJCNLP, 2019.
*J. Wei and K. Zou.*
[paper](https://www.aclweb.org/anthology/D19-1670.pdf)



## [Model](#content)

### Multitask Learning

1. **Multi-task transfer methods to improve one-shot learning for multimedia event detection,** in BMVC, 2015.
*W. Yan, J. Yap, and G. Mori.*
[paper](http://www.bmva.org/bmvc/2015/papers/paper037/index.html)

1. **Label efficient learning of transferable representations acrosss domains and tasks,** in NeurIPS, 2017.
*Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei.*
[paper](https://papers.nips.cc/paper/6621-label-efficient-learning-of-transferable-representations-acrosss-domains-and-tasks.pdf)

1. **Multi-content GAN for few-shot font style transfer,** in CVPR, 2018. 
*S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell.*
[paper](http://www.vovakim.com/papers/18_CVPRSpotlight_FontDropper.pdf)

1. **Feature space transfer for data augmentation,** in CVPR, 2018.
*B. Liu, X. Wang, M. Dixit, R. Kwitt, and N. Vasconcelos.* 
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Feature_Space_Transfer_CVPR_2018_paper.pdf)

1. **One-shot unsupervised cross domain translation,** in NeurIPS, 2018.
*S. Benaim and L. Wolf.* 
[paper](https://papers.nips.cc/paper/7480-one-shot-unsupervised-cross-domain-translation.pdf)

1. **Fine-grained visual categorization using meta-learning optimization with sample selection of auxiliary data,** in ECCV, 2018.
*Y. Zhang, H. Tang, and K. Jia.*
[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yabin_Zhang_Fine-Grained_Visual_Categorization_ECCV_2018_paper.pdf)

1. **Few-shot charge prediction with discriminative legal attributes,** in COLING, 2018.
*Z. Hu, X. Li, C. Tu, Z. Liu, and M. Sun.*
[paper](https://www.aclweb.org/anthology/C18-1041.pdf)

1. **Few-shot adversarial domain adaptation,** in NeurIPS, 2017.
*S. Motiian, Q. Jones, S. Iranmanesh, and G. Doretto.*
[paper](https://papers.nips.cc/paper/7244-few-shot-adversarial-domain-adaptation)

### Embedding Learning

1. **Object classification from a single example utilizing class relevance metrics,** in NeurIPS, 2005.*
*M. Fink.*
[paper](https://papers.nips.cc/paper/2576-object-classification-from-a-single-example-utilizing-class-relevance-metrics.pdf)

1. **Few-shot learning through an information retrieval lens,** in NeurIPS, 2017.
*E. Triantafillou, R. Zemel, and R. Urtasun.*
[paper](https://papers.nips.cc/paper/6820-few-shot-learning-through-an-information-retrieval-lens.pdf)

1. **Optimizing one-shot recognition with micro-set learning,** in CVPR, 2010.
*K. D. Tang, M. F. Tappen, R. Sukthankar, and C. H. Lampert.*
[paper](http://www.cs.ucf.edu/~mtappen/pubs/cvpr10_oneshot.pdf)

1. **Siamese neural networks for one-shot image recognition,** ICML deep learning workshop, 2015.
*G. Koch, R. Zemel, and R. Salakhutdinov*
[paper](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)

1. **Matching networks for one shot learning,** in NeurIPS, 2016.
*O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al.* 
[paper](https://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf)

1. **Learning feed-forward one-shot learners,** in NeurIPS, 2016.
*L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi.*
[paper](https://papers.nips.cc/paper/6068-learning-feed-forward-one-shot-learners.pdf)

1. **Low data drug discovery with one-shot learning,** ACS Central Science, 2017.
*H. Altae-Tran, B. Ramsundar, A. S. Pappu, and V. Pande.* 
[paper](https://arxiv.org/abs/1611.03199)

1.  **Prototypical networks for few-shot learning,** in NeurIPS, 2017.
*J. Snell, K. Swersky, and R. S. Zemel.*
[paper](https://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning.pdf)

1. **Attentive recurrent comparators,** in ICML, 2017.
*P. Shyam, S. Gupta, and A. Dukkipati.*
[paper](http://proceedings.mlr.press/v70/shyam17a/shyam17a.pdf)

1. **Learning algorithms for active learning,** in ICML, 2017.
*P. Bachman, A. Sordoni, and A. Trischler.*
[paper](http://proceedings.mlr.press/v70/bachman17a.pdf)

1. **Active one-shot learning,** arXiv preprint, 2017.
*M. Woodward and C. Finn.*
[paper](https://arxiv.org/abs/1702.06559)

1. **Structured set matching networks for one-shot part labeling,** in CVPR, 2018.
*J. Choi, J. Krishnamurthy, A. Kembhavi, and A. Farhadi.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Choi_Structured_Set_Matching_CVPR_2018_paper.pdf)

1. **Low-shot learning from imaginary data,** in CVPR, 2018.
*Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Low-Shot_Learning_From_CVPR_2018_paper.pdf)

1. **Learning to compare: Relation network for few-shot learning,** in CVPR, 2018.
*F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Sung_Learning_to_Compare_CVPR_2018_paper.pdf)

1. **Dynamic conditional networks for few-shot learning,** in ECCV, 2018.
*F. Zhao, J. Zhao, S. Yan, and J. Feng.*
[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Fang_Zhao_Dynamic_Conditional_Networks_ECCV_2018_paper.pdf)

1. **Tadam: Task dependent adaptive metric for improved few-shot learning,** in NeurIPS, 2018.
*B. Oreshkin, P. R. López, and A. Lacoste.*
[paper](https://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning.pdf)

1. **Meta-learning for semi- supervised few-shot classification,** in ICLR, 2018.
*M. Ren, S. Ravi, E. Triantafillou, J. Snell, K. Swersky, J. B. Tenen- baum, H. Larochelle, and R. S. Zemel.* 
[paper](https://openreview.net/forum?id=r1n5Osurf)

1. **Few-shot learning with graph neural networks,** in ICLR, 2018.
*V. G. Satorras and J. B. Estrach.*
[paper](https://openreview.net/pdf?id=HJcSzz-CZ)

1. **A simple neural attentive meta-learner,** in ICLR, 2018.
*N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel.*
[paper](https://openreview.net/forum?id=B1DmUzWAW)

1. **Meta-learning with differentiable closed-form solvers,** in ICLR, 2019.
*L. Bertinetto, J. F. Henriques, P. Torr, and A. Vedaldi.* 
[paper](https://openreview.net/forum?id=HyxnZh0ct7)

1. **Learning to propopagate labels: Transductive propagation network for few-shot learning,** in ICLR, 2019.
*Y. Liu, J. Lee, M. Park, S. Kim, E. Yang, S. Hwang, and Y. Yang.*
[paper](https://openreview.net/forum?id=SyVuRiC5K7)


### Learning with External Memory

1. **Meta-learning with memory-augmented neural networks,** in ICML, 2016.
*A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap.*
[paper](http://proceedings.mlr.press/v48/santoro16.pdf)

1. **Few-shot object recognition from machine-labeled web images,** in CVPR, 2017.
*Z. Xu, L. Zhu, and Y. Yang.*
[paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Xu_Few-Shot_Object_Recognition_CVPR_2017_paper.pdf)

1. **Learning to remember rare events,** in ICLR, 2017.
*Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio.*
[paper](https://openreview.net/forum?id=SJTQLdqlg)

1. **Meta networks,** in ICML, 2017.
*T. Munkhdalai and H. Yu.* 
[paper](http://proceedings.mlr.press/v70/munkhdalai17a/munkhdalai17a.pdf)

1. **Memory matching networks for one-shot image recognition,** in CVPR, 2018.
*Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cai_Memory_Matching_Networks_CVPR_2018_paper.pdf)

1. **Compound memory networks for few-shot video classification,** in ECCV, 2018.
*L. Zhu and Y. Yang.*
[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Linchao_Zhu_Compound_Memory_Networks_ECCV_2018_paper.pdf)

1. **Memory, show the way: Memory based few shot word representation learning,** in EMNLP, 2018.
*J. Sun, S. Wang, and C. Zong.*
[paper](https://www.aclweb.org/anthology/D18-1173.pdf)

1. **Rapid adaptation with conditionally shifted neurons,** in ICML, 2018.
*T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler.*
[paper](http://proceedings.mlr.press/v80/munkhdalai18a/munkhdalai18a.pdf)

1. **Adaptive posterior learning: Few-shot learning with a surprise-based memory module,** in ICLR, 2019. 
*T. Ramalho and M. Garnelo.*
[paper](https://openreview.net/forum?id=ByeSdsC9Km)



### Generative Modeling
1. **One-shot learning of object categories,** TPAMI, 2006.
*L. Fei-Fei, R. Fergus, and P. Perona.*
[paper](http://vision.stanford.edu/documents/Fei-FeiFergusPerona2006.pdf)

1. **Learning to learn with compound HD models,** in NeurIPS, 2011.
*A. Torralba, J. B. Tenenbaum, and R. R. Salakhutdinov.*
[paper](https://papers.nips.cc/paper/4474-learning-to-learn-with-compound-hd-models.pdf)

1. **One-shot learning with a hierarchical nonparametric bayesian model,** in ICML Workshop on Unsupervised and Transfer Learning, 2012.
*R. Salakhutdinov, J. Tenenbaum, and A. Torralba.*
[paper](http://proceedings.mlr.press/v27/salakhutdinov12a/salakhutdinov12a.pdf)

1. **Human-level concept learning through probabilistic program induction,** Science, 2015.
*B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum.*
[paper](https://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf)

1. **One-shot generalization in deep generative models,** in ICML, 2016.
*D. Rezende, I. Danihelka, K. Gregor, and D. Wierstra.*
[paper](https://arxiv.org/pdf/1603.05106)

1. **One-shot video object segmentation,** in CVPR, 2017.
*S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixe ́, D. Cremers,
and L. Van Gool.*
[paper](http://zpascal.net/cvpr2017/Caelles_One-Shot_Video_Object_CVPR_2017_paper.pdf)

1. **Towards a neural statistician,** in ICLR, 2017.
*H. Edwards and A. Storkey.*
[paper](https://openreview.net/forum?id=HJDBUF5le)

1. **Extending a parser to distant domains using a few dozen partially annotated examples,** in ACL, 2018.
*V. Joshi, M. Peters, and M. Hopkins.*
[paper](https://www.aclweb.org/anthology/P18-1110.pdf)

1. **MetaGAN: An adversarial approach to few-shot learning,** in NeurIPS, 2018.
*R. Zhang, T. Che, Z. Ghahramani, Y. Bengio, and Y. Song.*
[paper](https://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning.pdf)

1. **Few-shot autoregressive density estimation: Towards learning to learn distributions,** in ICLR, 2018.
*S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas.* 
[paper](https://openreview.net/forum?id=r1wEFyWCW)

1. **The variational homoencoder: Learning to learn high capacity generative models from few examples,** in UAI, 2018.
*L. B. Hewitt, M. I. Nye, A. Gane, T. Jaakkola, and J. B. Tenenbaum.*
[paper](http://auai.org/uai2018/proceedings/papers/351.pdf)

1. **Meta-learning probabilistic inference for prediction,** in ICLR, 2019.
*J. Gordon, J. Bronskill, M. Bauer, S. Nowozin, and R. Turner.*
[paper](https://openreview.net/forum?id=HkxStoC5F7)


## [Algorithm](#content)

### Refining Existing Parameters

1. **Cross-generalization: Learning novel classes from a single example by feature replacement,** in CVPR, 2005. 
*E. Bart and S. Ullman.*
[paper](http://www.inf.tu-dresden.de/content/institutes/ki/is/HS_SS08_Papers/BartUllmanCVPR05.pdf)

1. **One-shot adaptation of supervised deep convolutional models,** in ICLR, 2013.
*J. Hoffman, E. Tzeng, J. Donahue, Y. Jia, K. Saenko, and T. Darrell.*
[paper](https://openreview.net/forum?id=tPCrkaLa9Y5ld)

1. **Learning to learn: Model regression networks for easy small sample learning,** in ECCV, 2016.
*Y.-X. Wang and M. Hebert.*
[paper](https://ri.cmu.edu/pub_files/2016/10/yuxiongw_eccv16_learntolearn.pdf)

1. **Learning from small sample sets by combining unsupervised meta-training with CNNs,** in NeurIPS, 2016.
*Y.-X. Wang and M. Hebert.*
[paper](https://papers.nips.cc/paper/6408-learning-from-small-sample-sets-by-combining-unsupervised-meta-training-with-cnns)

1. **Efficient k-shot learning with regularized deep networks,** in AAAI, 2018.
*D. Yoo, H. Fan, V. N. Boddeti, and K. M. Kitani.*
[paper](https://arxiv.org/abs/1710.02277)

1. **CLEAR: Cumulative learning for one-shot one-class image recognition,** in CVPR, 2018.
*J. Kozerawski and M. Turk.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Kozerawski_CLEAR_Cumulative_LEARning_CVPR_2018_paper.pdf)

1. **Learning structure and strength of CNN filters for small sample size training,** in CVPR, 2018. 
*R. Keshari, M. Vatsa, R. Singh, and A. Noore.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Keshari_Learning_Structure_and_CVPR_2018_paper.pdf)

1. **Dynamic few-shot visual learning without forgetting,** in CVPR, 2018.
*S. Gidaris and N. Komodakis.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Gidaris_Dynamic_Few-Shot_Visual_CVPR_2018_paper.pdf)

1. **Low-shot learning with imprinted weights,** in CVPR, 2018.
*H. Qi, M. Brown, and D. G. Lowe.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Qi_Low-Shot_Learning_With_CVPR_2018_paper.pdf)

1. **Neural voice cloning with a few samples,** in NeurIPS, 2018.
*S.Arik,J.Chen,K.Peng,W.Ping,andY.Zhou.*
[paper](https://papers.nips.cc/paper/8206-neural-voice-cloning-with-a-few-samples.pdf)

### Refining Meta-learned Parameters

1.  **Model-agnostic meta-learning for fast adaptation of deep networks,** in ICML, 2017.
*C. Finn, P. Abbeel, and S. Levine.*
[paper](http://proceedings.mlr.press/v70/finn17a/finn17a.pdf?source=post_page---------------------------)

1. **Bayesian model-agnostic meta-learning,** in NeurIPS, 2018.
*J. Yoon, T. Kim, O. Dia, S. Kim, Y. Bengio, and S. Ahn.*
[paper](https://papers.nips.cc/paper/7963-bayesian-model-agnostic-meta-learning.pdf)

1. **Probabilistic model-agnostic meta-learning,** in NeurIPS, 2018.
*C. Finn, K. Xu, and S. Levine.*
[paper](https://papers.nips.cc/paper/8161-probabilistic-model-agnostic-meta-learning.pdf)

1. **Gradient-based meta-learning with learned layerwise metric and subspace,** in ICML, 2018.
*Y. Lee and S. Choi.*
[paper](http://proceedings.mlr.press/v80/lee18a/lee18a.pdf)

1. **Recasting gradient-based meta-learning as hierarchical Bayes,** in ICLR, 2018.
*E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths.*
[paper](https://openreview.net/forum?id=BJ_UL-k0b)

1. **Few-shot human motion prediction via meta-learning,** in ECCV, 2018.
*L.-Y. Gui, Y.-X. Wang, D. Ramanan, and J. Moura.*
[paper](http://openaccess.thecvf.com/content_ECCV_2018/papers/Liangyan_Gui_Few-Shot_Human_Motion_ECCV_2018_paper.pdf)

1. **The effects of negative adaptation in model-agnostic meta-learning,** arXiv preprint, 2018.
*T. Deleu and Y. Bengio.*
[paper](http://metalearning.ml/2018/papers/metalearn2018_paper76.pdf)

1. **Amortized bayesian meta-learning,** in ICLR, 2019.
*S. Ravi and A. Beatson.*
[paper](https://openreview.net/forum?id=rkgpy3C5tX)

1. **Meta-learning with latent embedding optimization,** in ICLR, 2019.
*A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell.* 
[paper](https://openreview.net/forum?id=BJgklhAcK7)


### Learning Search Steps

1. **Optimization as a model for few-shot learning,** in ICLR, 2017.
*S. Ravi and H. Larochelle.*
[paper](https://openreview.net/forum?id=rJY0-Kcll)


## [Applications](#content)


### Computer Vision

1. **Learning robust visual-semantic embeddings,** in CVPR, 2017.
*Y.-H. Tsai, L.-K. Huang, and R. Salakhutdinov.*
[paper](http://openaccess.thecvf.com/content_ICCV_2017/papers/Tsai_Learning_Robust_Visual-Semantic_ICCV_2017_paper.pdf)

1. **Multi-attention network for one shot learning,** in CVPR, 2017.
*P. Wang, L. Liu, C. Shen, Z. Huang, A. van den Hengel, and H. Tao Shen.*
[paper](http://zpascal.net/cvpr2017/Wang_Multi-Attention_Network_for_CVPR_2017_paper.pdf)

1. **One-shot action localization by learning sequence matching network,** in CVPR, 2018.
*H. Yang, X. He, and F. Porikli.*
[paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_One-Shot_Action_Localization_CVPR_2018_paper.pdf)

1. **Few-shot and zero-shot multi-label learning for structured label spaces,** in EMNLP, 2018.
*A. Rios and R. Kavuluru.*
[paper](https://www.aclweb.org/anthology/D18-1352.pdf)

1. **Meta-dataset: A dataset of datasets for learning to learn from few examples,** arXiv preprint, 2019.
*E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol et al.*
[paper](https://arxiv.org/abs/1903.03096)

### Robotics

1. **Towards one shot learning by imitation for humanoid robots,** in ICRA, 2010.
*Y. Wu and Y. Demiris.*
[paper](https://spiral.imperial.ac.uk/bitstream/10044/1/12669/4/icra2010.pdf)

1. **Learning manipulation actions from a few demonstrations,** in ICRA, 2013.
*N. Abdo, H. Kretzschmar, L. Spinello, and C. Stachniss.*
[paper](https://ieeexplore.ieee.org/document/6630734)

1. **Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approach,** in ICRA, 2016. 
*M. Hamaya, T. Matsubara, T. Noda, T. Teramae, and J. Morimoto.*
[paper](https://ieeexplore.ieee.org/document/7487509)

1. **One-shot imitation learning,** in NeurIPS, 2017.
*Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba.*
[paper](https://papers.nips.cc/paper/6709-one-shot-imitation-learning.pdf)

1. **Continuous adaptation via meta-learning in nonstationary and competitive environments,** in ICLR, 2018.
*M. Al-Shedivat, T. Bansal, Y. Burda, I. Sutskever, I. Mordatch, and P. Abbeel.*
[paper](https://openreview.net/forum?id=Sk2u1g-0-)

1. **Deep online learning via meta-learning: Continual adaptation for model-based RL,** in ICLR, 2018.
*A. Nagabandi, C. Finn, and S. Levine.*
[paper](https://openreview.net/references/pdf?id=ryuIpa6S4)

1. **Meta-learning language-guided policy learning,** in ICLR, 2019.
*J. D. Co-Reyes, A. Gupta, S. Sanjeev, N. Altieri, J. DeNero, P. Abbeel, and S. Levine.*
[paper](https://openreview.net/forum?id=HkgSEnA5KQ)

### Natural Language Processing

1. **High-risk learning: Acquiring new word vectors from tiny data,** in EMNLP, 2017.
*A. Herbelot and M. Baroni.*
[paper](https://www.aclweb.org/anthology/D17-1030.pdf)

1. **FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation,** in EMNLP, 2018.
*X. Han, H. Zhu, P. Yu, Z. Wang, Y. Yao, Z. Liu, and M. Sun.*
[paper](https://www.aclweb.org/anthology/D18-1514.pdf)

### Acoustic Signal Processing

1. **One-shot learning of generative speech concepts,** in CogSci, 2014. 
*B. Lake, C.-Y. Lee, J. Glass, and J. Tenenbaum.*
[paper](https://groups.csail.mit.edu/sls/publications/2014/lake-cogsci14.pdf)

1. **Machine speech chain with one-shot speaker adaptation,** INTERSPEECH, 2018.
*A. Tjandra, S. Sakti, and S. Nakamura.* 
[paper](https://ahcweb01.naist.jp/papers/conference/2018/201809_Interspeech_andros-tj/201809_Interspeech_andros-tj_1.paper.pdf)

1. **Investigation of using disentangled and interpretable representations for one-shot cross-lingual voice conversion,** INTERSPEECH, 2018.
*S. H. Mohammadi and T. Kim.*
[paper](https://isca-speech.org/archive/Interspeech_2018/pdfs/2525.pdf)

### Others

1. **A meta-learning perspective on cold-start recommendations for items,** in NeurIPS, 2017.
*M. Vartak, A. Thiagarajan, C. Miranda, J. Bratman, and H. Larochelle.*
[paper](https://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf)

1. **SMASH: One-shot model architecture search through hypernetworks,** in ICLR, 2018.
*A. Brock, T. Lim, J. Ritchie, and N. Weston.*
[paper](https://openreview.net/forum?id=rydeCEhs-)


## [Theories](#content)

1. **Learning to learn around a common mean,** in NeurIPS, 2018.
*G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil.* 
[paper](https://papers.nips.cc/paper/8220-learning-to-learn-around-a-common-mean.pdf)

1. **Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm,** in ICLR, 2018.
*C. Finn and S. Levine.*
[paper](https://openreview.net/forum?id=HyjC5yWCW)