Few Shot Learning using HRI https://github.com/MSiam/Few-Shot-Learning
Learning Embedding Adaptation for Few-Shot Learning https://github.com/Sha-Lab/FEAT
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning https://github.com/csyanbin/TPN
用PyTorch实现few-shot learning https://github.com/oscarknagg/few-shot
Few-Shot无监督图到图变换：从少量样本挖掘新事物特质 https://arxiv.org/abs/1905.01723 https://nvlabs.github.io/FUNIT/ https://github.com/nvlabs/FUNIT/
【Keras/Tensorflow 2实现的少样本学习算法】’keras-fsl - Some State-of-the-Art few shot learning algorithms in tensorflow 2' https://github.com/few-shot-learning/Keras-FewShotLearning
【单样本(One-shot)学习】《One-shot learning》 https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Fheartbeat.fritz.ai%2Fone-shot-learning-part-1-2-definitions-and-fundamental-techniques-1df944e5836a https://medium.com/m/global-identity?redirectUrl=https%3A%2F%2Fheartbeat.fritz.ai%2Fone-shot-learning-part-2-2-facial-recognition-using-a-siamese-network-5aee53196255
《少样本学习 by Massimiliano Patacchiola》 https://www.bilibili.com/video/BV1jC4y1W7uX/
The code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.
https://github.com/WenbinLee/DN4
Cross-Domain Few-Shot Learning Benchmarking System https://github.com/IBM/cdfsl-benchmark
https://github.com/Goldesel23/Siamese-Networks-for-One-Shot-Learning https://sorenbouma.github.io/blog/oneshot/
[TOG(SIGGRAPH Asia) 2019] Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning https://github.com/hologerry/AGIS-Net
https://github.com/mileyan/simple_shot
https://github.com/xinzheli1217/learning-to-self-train
https://github.com/bingykang/Fewshot_Detection
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment https://github.com/kaixin96/PANet
Paper:Few-Shot Text Classification with Induction Network https://github.com/wuzhiye7/Induction-Network-on-FewRel
Diversity Transfer Network for Few-Shot Learning https://github.com/Yuxin-CV/DTN
S2M2 Charting the Right Manifold: Manifold Mixup for Few-shot Learning https://github.com/nupurkmr9/S2M2_fewshot
Cross Attention Network for Few-shot Classification https://github.com/blue-blue272/fewshot-CAN
An unofficial implementation of MemoPainter(Coloring With Limited Data: Few-shot Colorization via Memory Augmented Networks) using PyTorch framework. https://github.com/dongheehand/MemoPainter-PyTorch
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (ICLR 2020 spotlight) https://github.com/hytseng0509/CrossDomainFewShot
https://github.com/google-research/fixmatch
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation https://github.com/HKUSTCV/FSS-1000
Adaptive Cross-Modal Few-shot learning OSS code https://github.com/ElementAI/am3
A PyTorch implementation of "Image Deformation Meta-Networks for One-Shot Learning"(CVPR 2019 Oral). https://github.com/tankche1/IDeMe-Net
The source code of our COLING'18 paper "Few-Shot Charge Prediction with Discriminative Legal Attributes". https://github.com/thunlp/attribute_charge
Learning Rich Features at High-Speed for Single-Shot Object Detection, ICCV, 2019 https://github.com/vaesl/LRF-Net
Boosting Few-Shot Visual Learning with Self-Supervision https://github.com/valeoai/BF3S
Pytorch implementation of "Few-Shot Unsupervised Image-to-Image Translation" (ICCV 2019) https://github.com/znxlwm/FUNIT-pytorch
The codes for paper "Continual Local Replacement for Few-shot Image Recognition" https://github.com/Lecanyu/ContinualLocalReplacement
https://github.com/microsoft/metric-transfer.pytorch
【小数据如何深度学习】《How To Use Deep Learning Even with Small Data - Towards Data Science》 https://towardsdatascience.com/how-to-use-deep-learning-even-with-small-data-e7f34b673987
《All you need is a good representation: A multi-level and classifier-centric representation for few-shot learning》 https://arxiv.org/abs/1911.12476
Few-Shot-Object-Detection-Dataset https://github.com/fanq15/Few-Shot-Object-Detection-Dataset
Code for Paper "Incremental Few-Shot Learning with Attention Attractor Networks" https://github.com/renmengye/inc-few-shot-attractor-public
【少样本语义分割文献列表】 https://github.com/xiaomengyc/Few-Shot-Semantic-Segmentation-Papers
【少样本/零样本学习文献资源列表】 https://github.com/e-271/awesome-few-shot-learning
【少样本学习相关文献列表】 https://github.com/Duan-JM/awesome-papers-fewshot
用十亿级半监督学习实现最先进图像与视频分类 https://ai.facebook.com/blog/billion-scale-semi-supervised-learning/
'Multi-digit MNIST for Few-shot Learning - Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning' https://github.com/shaohua0116/MultiDigitMNIST
N样本学习：用更少的数据，学到更多 https://blog.floydhub.com/n-shot-learning/
Few-Shot Image Recognition by Predicting Parameters from Activations https://github.com/joe-siyuan-qiao/FewShot-CVPR
Context-Aware Zero-Shot Recognition https://github.com/ruotianluo/Context-aware-ZSR
A combination of Autoencoder and Robust PCA https://github.com/zc8340311/RobustAutoencoder
The code and models for paper: "ScratchDet: Exploring to Train Single-Shot Object Detectors from Scratch" https://github.com/KimSoybean/ScratchDet The code is coming soon.
Pytorch implementation of Paper "Discovering Visual Patterns in Art Collections with Spatially-consistent Feature Learning" http://imagine.enpc.fr/~shenx/ArtMiner/ https://github.com/XiSHEN0220/ArtMiner
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples https://github.com/google-research/meta-dataset
TensorFlow implementation for "Meta-Transfer Learning for Few-Shot Learning" (CVPR 2019) https://github.com/y2l/meta-transfer-learning-tensorflow
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models
Implementation of "A Simple Neural Attentive Meta-Learner" (SNAIL, https://arxiv.org/pdf/1707.03141.pdf) in PyTorch https://github.com/eambutu/snail-pytorch
Translate images to unseen domains in the test time with few example images. https://nvlabs.github.io/FUNIT/ https://github.com/NVlabs/FUNIT
【少样本学习文献列表】’Few-Shot Papers - This repository contains few-shot learning (FSL) papers mentioned in our FSL survey.' https://github.com/tata1661/FewShotPapers
【Meta-Dataset：面向少样本学习的数据集】《Announcing Meta-Dataset: A Dataset of Datasets for Few-Shot Learning | Google AI Blog》 https://ai.googleblog.com/2020/05/announcing-meta-dataset-dataset-of.html
《Few-Shot learning with Reptile》 https://keras.io/examples/vision/reptile/
《Wandering Within a World: Online Contextualized Few-Shot Learning》 https://github.com/renmengye/oc-fewshot-public https://www.arxiv-vanity.com/papers/2007.04546/
《Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild》 http://imagine.enpc.fr/~xiaoy/FSDetView/
A New Meta-Baseline for Few-Shot Learning. https://github.com/cyvius96/few-shot-meta-baseline
Implementations of few-shot object detection benchmarks https://github.com/ucbdrive/few-shot-object-detection
PyTorch implementation of "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning" https://github.com/yaoyao-liu/E3BM
A pytorch implementation of "Domain-Adaptive Few-Shot Learning" https://github.com/dingmyu/DAPN
Code for the paper "Selecting Relevant Features from a Universal Representation for Few-shot Classification" https://github.com/dvornikita/SUR
PyTorch implementation of “Negative Margin Matters: Understanding Margin in Few-shot Classification” https://github.com/bl0/negative-margin.few-shot
(CVPR2020) Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition https://github.com/Tsingularity/PoseNorm_Fewshot
This repository contains the official implementation of DPGN: Distribution Propagation Graph Network for Few-shot Learning. https://github.com/megvii-research/dpgn
This repository contains the code for our paper "Instance Credibility Inference for Few-Shot Learning" in CVPR, 2020. https://github.com/Yikai-Wang/ICI-FSL
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector https://github.com/fanq15/FSOD-code
Transductive Few-shot Learning with Meta-Learned Confidence https://github.com/seongmin-kye/MCT_DFMN
Context-Transformer: Tackling Object Confusion for Few-Shot Detection, AAAI 2020 https://github.com/Ze-Yang/Context-Transformer
A Universal Representation Transformer Layer for Few-Shot Image Classification https://github.com/liulu112601/URT
(ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild" https://github.com/YoungXIAO13/FewShotDetection
Official repository for Few-Shot Class-Incremental Learning (FSCIL) https://github.com/xyutao/fscil
(ECCV 2020) PyTorch implementation of paper "Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild" https://github.com/YoungXIAO13/FewShotViewpoint
https://github.com/fanq15/FewX
Code for paper "DeepEMD: Few-Shot Image Classification with Differentiable Earth Mover's Distance and Structured Classifiers", CVPR2020 https://github.com/icoz69/DeepEMD
(CVPR2020) Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition https://github.com/Tsingularity/PoseNorm_Fewshot
Generalizing from a Few Examples: A Survey on Few-Shot Learning https://arxiv.org/abs/1904.05046
An Overview of Deep Learning Architectures in Few-Shot Learning Domain https://github.com/shruti-jadon/Hands-on-One-Shot-Learning
Transductive Information Maximization for Few-Shot Learning https://github.com/mboudiaf/TIM
Hyperbolic Visual Embedding Learning for Zero-Shot Recognition (CVPR 2020) https://github.com/ShaoTengLiu/Hyperbolic_ZSL
Few-shot learning framework for opinion summarization published at EMNLP 2020. https://github.com/abrazinskas/FewSum
Few-Shot Learning with Global Class Representations https://github.com/tiangeluo/fsl-global
infinite mixture prototypes for few-shot learning https://github.com/k-r-allen/imp
Improved Few-Shot Visual Classification https://github.com/peymanbateni/simple-cnaps
[ICLR2021 Oral] Free Lunch for Few-Shot Learning: Distribution Calibration https://github.com/ShuoYang-1998/Few_Shot_Distribution_Calibration
BriNet: Towards Bridging the Intra-class andInter-class Gaps in One-Shot Segmentation https://github.com/Wi-sc/BriNet
A New Meta-Baseline for Few-Shot Learning https://github.com/yinboc/few-shot-meta-baseline
PyTorch code for NeurIPSW 2020 paper (4th Workshop on Meta-Learning) "Few-Shot Unsupervised Continual Learning through Meta-Examples" https://github.com/alessiabertugli/FUSION
Official codes for 'Multi-Domain Learning for Accurate and Few-Shot Color Constancy' https://github.com/msxiaojin/MDLCC
PyTorch implementation for "Few-Shot Learning with Class Imbalance" https://github.com/mattochal/imbalanced_fsl_public
[NeurIPS 2020] Released code for Interventional Few-Shot Learning https://github.com/yue-zhongqi/ifsl
Implementations of many meta-learning algorithms to solve the few-shot learning problem in Pytorch https://github.com/cnguyen10/few_shot_meta_learning
少样本图像生成文献资料列表 https://github.com/bcmi/Awesome-Few-Shot-Image-Generation
Counterfactual Zero-Shot and Open-Set Visual Recognition https://github.com/yue-zhongqi/gcm-cf
ZenNAS: A Zero-Shot NAS for High-Performance Deep Image Recognition https://github.com/idstcv/ZenNAS
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding https://www.arxiv-vanity.com/papers/2103.05950
Few-shot Image Generation via Cross-domain Correspondence https://www.arxiv-vanity.com/papers/2104.06820
FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding (CVPR 2021) github.com/MegviiDetection/FSCE
Few-Shot Video Object Detection github.com/fanq15/FewX
Few-shot Classification via Adaptive Attention github.com/zihangJiang/Adaptive-Attention
《Few-Shot Open-Set Recognition with Meta-Learning》(CVPR 2020) github.com/BoLiu-SVCL/meta-open
《Open World Compositional Zero-Shot Learning》(CVPR 2021) github.com/ExplainableML/czsl
零样本目标检测相关资源大列表 github.com/KennithLi/Awesome-Zero-Shot-Object-Detection
不确定性感知小样本图像分类模型，实现SOTA性能 https://weibo.com/ttarticle/p/show?id=2309404671955579306025
Few-shot Visual Relationship Co-localization github.com/vl2g/VRC
LibFewShot：少样本学习综合库 github.com/RL-VIG/LibFewShot
少样本目标检测相关进展及资源列表 github.com/gabrielhuang/awesome-few-shot-object-detection
mmfewshot：OpenMMLab少样本学习工具包和基准 github.com/open-mmlab/mmfewshot
Easy Few-Shot Learning：少样本图像分类参考代码和教程 github.com/sicara/easy-few-shot-learning
Label, Verify, Correct: A Simple Few Shot Object Detection Method https://arxiv.org/abs/2112.05749
Few-Shot-Intent-Detection：少样本意图检测相关资源集 github.com/jianguoz/Few-Shot-Intent-Detection
FSL-Mate：少样本学习(FSL)资源集合 github.com/tata1661/FSL-Mate
Subspace Regularizers for Few-Shot Class Incremental Learning https://arxiv.org/abs/2110.07059
https://arxiv.org/abs/2201.10728
Corrupted Image Modeling for Self-Supervised Visual Pre-Training https://arxiv.org/abs/2202.03382
Few-shot Learning with Noisy Labels https://arxiv.org/abs/2204.05494
It's DONE: Direct ONE-shot learning without training optimization https://arxiv.org/abs/2204.13361
Few-Shot Diffusion Models https://arxiv.org/abs/2205.15463
CyCLIP: Cyclic Contrastive Language-Image Pretraining https://arxiv.org/abs/2205.14459
[CL]《Promptagator: Few-shot Dense Retrieval From 8 Examples》Z Dai, V Y. Zhao, J Ma, Y Luan, J Ni, J Lu, A Bakalov, K Guu, K B. Hall, M Chang [Google Research] (2022) https://arxiv.org/abs/2209.11755