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https://github.com/indussky8/awesome-few-shot-learning

A review for latest few-shot learning works
https://github.com/indussky8/awesome-few-shot-learning
List: awesome-few-shot-learning
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A review for latest few-shot learning works
Host: GitHub
URL: https://github.com/indussky8/awesome-few-shot-learning
Owner: indussky8
License: mit
Created: 2020-11-30T07:58:55.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2021-10-12T10:31:14.000Z (about 3 years ago)
Last Synced: 2024-05-23T03:00:51.935Z (7 months ago)
Size: 9.38 MB
Stars: 123
Watchers: 5
Forks: 21
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
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ultimate-awesome - awesome-few-shot-learning - A review for latest few-shot learning works. (Other Lists / Monkey C Lists)
README

        # Few-Shot Learning

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

A curated list of resources including papers, comparitive results on standard datasets and relevant links pertaining to few-shot learning.  

## Contributing

Contributions are welcome. If you have suggestions for new sections or valuable works to be included, please feel free to raise an issue and discuss in [issue](https://github.com/indussky8/awesome-few-shot-learning/issues) module.

## Table of Contents

+ [Papers](#Papers)

+ [Datasets](#Datasets)

+ [Starter code for FSL](#Starter-Code)

+ [Other Resources](#Other-resources)

## Few-Shot Learning (Classification)

### Papers

#### [1] Multi-domain based FSL [[Website]](https://github.com/google-research/meta-dataset) 

+ **Meta-Dataset**: Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. "Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples." ICLR (2020). [[pdf]](https://openreview.net/pdf?id=rkgAGAVKPr) [[code]](https://github.com/google-research/meta-dataset/tree/master/meta_dataset/dataset_conversion/splits).

+ **SimpleCNAPS**: Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal. "Improved Few-Shot Visual Classification." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf) [[code]](https://github.com/peymanbateni/simple-cnaps). 

+ **SUR**: Nikita Dvornik, Cordelia Schmid, and Julien Mairal. "Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550766.pdf) [[code]](https://github.com/dvornikita/SUR). 

  

+ **URT**: Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle. "A Universal Representation Transformer Layer for Few-Shot Image Classification." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=04cII6MumYV)[[code]](https://github.com/liulu112601/URT). 

 

+ **FLUTE**: Eleni Triantafillou, Hugo Larochelle, Richard Zemel, Vincent Dumoulin. "Learning a Universal Template for Few-shot Dataset Generalization." ICML(2021). [[pdf]](https://arxiv.org/pdf/2105.07029.pdf). 

+ **URL**: Wei-Hong Li, Xialei Liu, Hakan Bilen. "Universal Representation Learning from Multiple Domains for Few-shot Classification." ICCV(2021). [[pdf]](https://arxiv.org/pdf/2103.13841.pdf). 

+ **ITA**: Wei-Hong Li, Xialei Liu, Hakan Bilen. "Improving Task Adaptation for Cross-domain Few-shot Learning." arXiv(2021). [[pdf]](https://arxiv.org/pdf/2107.00358.pdf). 

#### [2] Singal-domain based FSL

+ **LibFewShot**: wenbin Li, Chuanqi Dong, Pinzhuo Tian, Tiexin Qin, Xuesong Yang, Ziyi Wang, Jing Huo, Yinghuan Shi, Lei Wang, Yang Gao, Jiebo Luo. "LibFewShot: A Comprehensive Library for Few-shot Learning." arXiv(2021). [[pdf]](https://arxiv.org/pdf/2109.04898.pdf) [[code]](https://github.com/RL-VIG/LibFewShot). ``

  

  

#### Meta-Learning + data augmentation

+ **KIP**: Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. "Dataset Meta-Learning from Kernel Ridge-Regression." ICLR(2021). [[pdf]](https://openreview.net/forum?id=l-PrrQrK0QR) [[code]](https://colab.research.google.com/github/google-research/google-research/blob/master/kip/KIP.ipynb).

  

  

+ **ICLR withdraw**: Jialin Liu, Fei Chao, and Chih-Min Lin. "Task Level Data Augmentation for Meta-Learning." ICLR withdraw(2021). [[pdf]](https://openreview.net/forum?id=Hkx9UaNKDH).

+ **ICLR withdraw**: Renkun Ni, Micah Goldblum, Amr Sharaf, Kezhi Kong, and Tom Goldstein. "Data Augmentation for Meta-Learning." ICLR withdraw(2021). [[pdf]](https://openreview.net/forum?id=V2v7QcVkbhH).

+ **ICLR reject**: Xingjian Li, Haoyi Xiong, Haozhe An, Cheng-zhong Xu, and Dejing Dou. "XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-Domain Mixup." ICLR reject(2021). [[pdf]](https://openreview.net/forum?id=pD9x3TmLONE).

+ **ICLR reject**: Georgios Batzolis, Alberto Bernacchia, Da-shan Shiu, Michael Bromberg, and Alexandru Cioba. "Optimal allocation of data across training tasks in meta-learning." ICLR reject(2021). [[pdf]](https://openreview.net/forum?id=a4E6SL1rG3F).

#### ICLR 2021

+ **DC**: Shuo Yang, Lu Liu, and Min Xu. "Free Lunch for Few-shot Learning: Distribution Calibration." ICLR oral(2021). [[pdf]](https://openreview.net/pdf?id=JWOiYxMG92s) [[code]](https://github.com/ShuoYang-1998/ICLR2021-Oral_Distribution_Calibration).

  

  

+ **STARTUP**: Cheng Perng Phoo, and Bharath Hariharan. "Self-training For Few-shot Transfer Across Extreme Task Differences." ICLR oral(2021). [[pdf]](https://openreview.net/pdf?id=O3Y56aqpChA).

  

  

+ **CPM**: Mengye Ren, Michael Louis Iuzzolino, Michael Curtis Mozer, and Richard Zemel. "Wandering within a world: Online contextualized few-shot learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=oZIvHV04XgC).

 

  

  

+ **THEORY**: Simon Shaolei Du, Wei Hu, Sham M. Kakade, Jason D. Lee, and Qi Lei. "Few-Shot Learning via Learning the Representation, Provably." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=pW2Q2xLwIMD).

 

+ **URT**: Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, and Hugo Larochelle. "A Universal Representation Transformer Layer for Few-Shot Image Classification." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=04cII6MumYV). 

 

  

  

+ **COMET**: Kaidi Cao, Maria Brbic, and Jure Leskovec. "Concept Learners for Few-Shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=eJIJF3-LoZO).

 

  

  

+ **IEPT**: Manli Zhang, Jianhong Zhang, Zhiwu Lu, Tao Xiang, Mingyu Ding, and Songfang Huang. "IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=xzqLpqRzxLq).

 

  

 

+ **IDLVQ-C**: Kuilin Chen, and Chi-Guhn Lee. "Incremental few-shot learning via vector quantization in deep embedded space." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=3SV-ZePhnZM). ``

  

+ **SLE**: Bingchen Liu, Yizhe Zhu, Kunpeng Song, and Ahmed Elgammal. "Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=1Fqg133qRaI).

  

+ **repurposing MAML**: Namyeong Kwon, Hwidong Na, Gabriel Huang, and Simon Lacoste-Julien. "Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=qkLMTphG5-h) [[code]](https://anonymous.4open.science/r/08ef52cf-456a-4e36-a408-04e1ad0bc5a9/).

  

  

+ **MELR**: Nanyi Fei, Zhiwu Lu, Tao Xiang, and Songfang Huang. "MELR: Meta-Learning via Modeling Episode-Level Relationships for Few-Shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=D3PcGLdMx0).

  

  

+ **MB(Supervised)**: Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley, and Katerina Fragkiadaki. "Disentangling 3D Prototypical Networks for Few-Shot Concept Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=-Lr-u0b42he).

  

+ **MetaNorm**: Yingjun Du, Xiantong Zhen, Ling Shao, and Cees G. M. Snoek. "MetaNorm: Learning to Normalize Few-Shot Batches Across Domains." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=9z_dNsC4B5t).``

 

  

+ **ConstellationNet**: Weijian Xu, yifan xu, Huaijin Wang, and Zhuowen Tu. "Constellation Nets for Few-Shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=vujTf_I8Kmc).

 

  

+ **OVE PG GP + Cosine**: Jake Snell, and Richard Zemel. "Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=lgNx56yZh8a).

  

+ **BOIL**: Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim, and Se-Young Yun. "BOIL: Towards Representation Change for Few-shot Learning." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=umIdUL8rMH).

 

  

+ **FBNet**: Zhipeng Bao, Yu-Xiong Wang, and Martial Hebert. "Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis." ICLR(2021). [[pdf]](https://openreview.net/pdf?id=ESG-DMKQKsD). ``

 

  

  

 

#### NIPS 2020 

+ **DKT**: Patacchiola, Massimiliano and Turner, Jack and Crowley, Elliot J and O'Boyle, Michael and Storkey, Amos. "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels." NIPS (2020). [[pdf]](https://proceedings.neurips.cc//paper/2020/file/b9cfe8b6042cf759dc4c0cccb27a6737-Paper.pdf) [[code]](https://github.com/BayesWatch/deep-kernel-transfer).

  

  

#### ECCV 2020 

+ **MABAS**: Jaekyeom Kim, Hyoungseok Kim, and Gunhee Kim. "Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot learning." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460579.pdf) [[code]](https://github.com/jaekyeom/MABAS).

  

  

+ **centroid alignment**: Arman Afrasiyabi, Jean-François Lalonde, and Christian Gagné. "Associative Alignment for Few-shot Image Classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500018.pdf) [[code]](https://lvsn.github.io/associative-alignment/).

   

   

+ **TAFSSL**: Moshe Lichtenstein, Prasanna Sattigeri, Rogerio Feris, Raja Giryes, and Leonid Karlinsky. "TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520511.pdf). ````

   

  

+ **BD-CSPN**: Jinlu Liu, Liang Song, and Yongqiang Qin. "Prototype Rectification for Few-Shot Learning." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460715.pdf). ````

  

   

  

+ **SSL-FSL**: Jong-Chyi Su, Subhransu Maji, and Bharath Hariharan. "When Does Self-supervision Improve Few-shot Learning?." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520630.pdf). ``

  

  

+ **IDA**: Qing Liu, Orchid Majumder, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, and Stefano Soatto. "Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123520664.pdf). ``

  

  

+ **LS-FSL**: Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, and Meng Wang. "Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550715.pdf).

  

+ **SUR**: Nikita Dvornik, Cordelia Schmid, and Julien Mairal. "Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550766.pdf) [[code]](https://github.com/dvornikita/SUR). ``

  

+ **LR-distill**: Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, and Phillip Isola. "Rethinking Few-shot Image Classification: A Good Embedding is All You Need?." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590256.pdf) [[code]](https://github.com/WangYueFt/rfs/). `` ``

  

  

+ **E³BM**: Yaoyao Liu, Bernt Schiele, and Qianru Sun. "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610392.pdf) [[code]](https://gitlab.mpi-klsb.mpg.de/yaoyaoliu/e3bm). ``

  

  

+ **Data Design**: Othman Sbai, Camille Couprie, and Mathieu Aubry. "Impact of base dataset design on few-shot image classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610579.pdf) [[code]](http://imagine.enpc.fr/~sbaio/publications/fewshot_dataset_design/index.html). ``

  

  

+ **SEN**: Van Nhan Nguyen, Sigurd Løkse, Kristoffer Wickstrøm, Michael Kampffmeyer, Davide Roverso, and Robert Jenssen. "SEN: A Novel Feature Normalization Dissimilarity Measure for Prototypical Few-Shot Learning Networks." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680120.pdf). 

  

  

+ **EPNet**: Pau Rodríguez, Issam Laradji, Alexandre Drouin, and Alexandre Lacoste. "Embedding Propagation: Smoother Manifold for Few-Shot Classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123710120.pdf) [[code]](https://github.com/ElementAI/embedding-propagation). ````

  

  

+ **BSCD-FSL**: Yunhui Guo, Noel C. Codella, Leonid Karlinsky, and James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, Rogerio Feris. "A Broader Study of Cross-Domain Few-Shot Learning." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720120.pdf) [[code]](https://github.com/IBM/cdfsl-benchmark). ``

  

  

+ **DeepCaps-FSL**: Fangyu Wu, Jeremy S.Smith, Wenjin Lu, Chaoyi Pang, and Bailing Zhang. "Attentive Prototype Few-shot Learning with Capsule Network-based Embedding." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730239.pdf). ``

  

  

+ **Neg-Cosine**: Bin Liu, Yue Cao, Yutong Lin, Qi Li, Zheng Zhang, Mingsheng Long, and Han Hu. "Negative Margin Matters: Understanding Margin in Few-shot Classification." ECCV (2020). [[pdf]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490426.pdf) [[code]](https://github.com/bl0/negative-margin.few-shot.).

  

  

#### ICLR 2020

+ **ProtoNet-EST**: Tianshi Cao, Marc T Law, and Sanja Fidler. "A Theoretical Analysis of the Number of Shots in Few-Shot Learning." ICLR (2020). [[pdf]](https://openreview.net/attachment?id=HkgB2TNYPS&name=original_pdf).

+ **Transductive ﬁne-tuning**: Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto. "A Baseline for Few-Shot Image Classification." ICLR (2020). [[pdf]](https://openreview.net/attachment?id=rylXBkrYDS&name=original_pdf). ``

+ **LFT-FSL**: Hung-Yu Tseng, Hsin-Ying Lee, Jia-Bin Huang, and Ming-Hsuan Yang. "Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation." ICLR (2020). [[pdf]](https://openreview.net/pdf?id=SJl5Np4tPr) [[code]](https://github.com/hytseng0509/CrossDomainFewShot).

  

  

+ **Meta-Dataset**: Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle. "Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples." ICLR (2020). [[pdf]](https://openreview.net/pdf?id=rkgAGAVKPr) [[code]](https://github.com/google-research/meta-dataset/tree/master/meta_dataset/dataset_conversion/splits).

+ **SIB**: Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil Lawrence, and Andreas Damianou. "Empirical Bayes Transductive Meta-Learning with Synthetic Gradients." ICLR (2020). [[pdf]](https://openreview.net/pdf?id=Hkg-xgrYvH) [[code]](https://github.com/amzn/xfer). ``

  

  

  

  

#### CVPR 2020

+ **Selection-FSL**: Linjun Zhou, Peng Cui, Xu Jia, Shiqiang Yang, and Qi Tian. "Learning to Select Base Classes for Few-Shot Classification." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Learning_to_Select_Base_Classes_for_Few-Shot_Classification_CVPR_2020_paper.pdf). ``

+ **DSN**: Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. "Adaptive Subspaces for Few-Shot Learning." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Learning_to_Select_Base_Classes_for_Few-Shot_Classification_CVPR_2020_paper.pdf) [[code]](https://github.com/chrysts/dsn_fewshot). ````

  

  

 

+ **Few-Shot Open-set**: Bo Liu, Hao Kang, Haoxiang Li, Gang Hua, and Nuno Vasconcelos. "Few-Shot Open-Set Recognition using Meta-Learning." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Few-Shot_Open-Set_Recognition_Using_Meta-Learning_CVPR_2020_paper.pdf). ``

  

  

+ **FEAT**: Han-Jia Ye, Hexiang Hu, De-Chuan Zhan, and Fei Sha. "Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Ye_Few-Shot_Learning_via_Embedding_Adaptation_With_Set-to-Set_Functions_CVPR_2020_paper.pdf) [[code]](https://github.com/Sha-Lab/FEAT). `` ``

  

 

+ **Simple CNAPS**: Bateni, Peyman and Goyal, Raghav and Masrani, Vaden and Wood, Frank and Sigal, Leonid. "Improved Few-Shot Visual Classification." CVPR (2020). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Bateni_Improved_Few-Shot_Visual_Classification_CVPR_2020_paper.pdf) ``

  

  

#### ICML 2020 

+ **TaskNorm**: Bronskill, John and Gordon, Jonathan and Requeima, James and Nowozin, Sebastian and Turner, Richard. "TaskNorm: Rethinking Batch Normalization for Meta-Learning." ICML (2020). [[pdf]](http://proceedings.mlr.press/v119/bronskill20a/bronskill20a.pdf) [[code]](https://github.com/cambridge-mlg/cnaps).``

#### arxiv 2020 

+ **Meta-Baseline**: Yinbo Chen, Xiaolong Wang, Zhuang Liu, Huijuan Xu, and Trevor Darrell. "A New Meta-Baseline for Few-Shot Learning" arxiv (2020). [[pdf]](https://arxiv.org/pdf/2003.04390.pdf) [[code]](https://github.com/yinboc/few-shot-meta-baseline).``

+ **HPO**: Saikia, Tonmoy and Brox, Thomas and Schmid, Cordelia. "Optimized Generic Feature Learning for Few-shot Classification across Domains." arxiv (2020). [[pdf]](https://arxiv.org/pdf/2001.07926.pdf).``

  

  

#### NIPS 2019 

+ **CNAPS**: Jaekyeom Kim, Hyoungseok Kim, and Gunhee Kim. "Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes." NIPS (2019). [[pdf]](https://arxiv.org/pdf/1906.07697.pdf) [[code]](https://github.com/cambridge-mlg/cnaps).``

  

  

 + **LST**: Li, Xinzhe and Sun, Qianru and Liu, Yaoyao and Zhou, Qin and Zheng, Shibao and Chua, Tat-Seng and Schiele, Bernt. "Learning to self-train for semi-supervised few-shot classification." NIPS (2019). [[pdf]](https://proceedings.neurips.cc/paper/2019/file/bf25356fd2a6e038f1a3a59c26687e80-Paper.pdf) [[code]](https://github.com/xinzheli1217/learning-to-self-train).

  

  

 #### CVPR 2019

Li, Aoxue and Luo, Tiange and Lu, Zhiwu and Xiang, Tao and Wang, Liwei. "Large-scale few-shot learning: Knowledge transfer with class hierarchy." CVPR(2019). [[pdf]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Large-Scale_Few-Shot_Learning_Knowledge_Transfer_With_Class_Hierarchy_CVPR_2019_paper.pdf) [[code]](https://github.com/tiangeluo/fsl-hierarchy).

  

  

  

### Datasets

+ **miniImageNet:** [[link]](https://drive.google.com/file/d/1fJAK5WZTjerW7EWHHQAR9pRJVNg1T1Y7/view)

+ **tieredImageNet:** [[link]](https://github.com/kingfou/tieredImageNet)

+ **CUB:** [[link]](https://drive.google.com/file/d/1hbzc_P1FuxMkcabkgn9ZKinBwW683j45/view)

+ **Meta-Dataset:** [[link]](https://github.com/google-research/meta-dataset)

+ **CIFAR-FS:** [[link]](https://drive.google.com/file/d/1GjGMI0q3bgcpcB_CjI40fX54WgLPuTpS/view)

+ **FC100:** [[link]](https://drive.google.com/file/d/1_ZsLyqI487NRDQhwvI7rg86FK3YAZvz1/view)

# There are two datasets, Categories: 17 and 102. [[link]](http://www.robots.ox.ac.uk/~vgg/data/flowers/)

### Starter Code

There are several backbones: 

+ **Conv4:** [[link]](https://github.com/facebookresearch/fewshotDatasetDesign/blob/master/archs/Conv4_CL.py)

+ **Resnet10:** [[link]](https://github.com/facebookresearch/fewshotDatasetDesign/blob/a84094a0f6027d23be167295f59b5591b3eaef82/cl_fsl/io_utils.py)

+ **Resnet18:** [[link]](https://github.com/facebookresearch/fewshotDatasetDesign/blob/a84094a0f6027d23be167295f59b5591b3eaef82/cl_fsl/io_utils.py)

+ **WRN:** [[link]](https://github.com/facebookresearch/fewshotDatasetDesign/blob/master/archs/wide_resnet.py)

## Other resources

Please click [[here]](https://few-shot.yyliu.net/miniimagenet.html) for few-shot classification leaderboard.

## License

[![CC0](http://mirrors.creativecommons.org/presskit/buttons/88x31/svg/cc-zero.svg)](https://creativecommons.org/publicdomain/zero/1.0/)