Few-Shot Image Classification Edit Task

Methodology • Meta-Learning

papers with code

benchmarks

datasets

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Few-shot image classification is the task of doing image classification with only a few examples for each category (typically < 6 examples).

( Image credit: Learning Embedding Adaptation for Few-Shot Learning )

Benchmarks

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DATASET	BEST METHOD	PAPER TITLE	COMPARE
Mini-Imagenet 5-way (1-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
Mini-Imagenet 5-way (5-shot)	AmdimNet	Self-Supervised Learning For Few-Shot Image Classification	See all
Tiered ImageNet 5-way (1-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
Tiered ImageNet 5-way (5-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
OMNIGLOT - 1-Shot, 20-way	GCR	Few-Shot Learning with Global Class Representations	See all
OMNIGLOT - 5-Shot, 20-way	MC2+	Meta-Curvature	See all
CIFAR-FS 5-way (5-shot)	LST+MAP	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network	See all
CUB 200 5-way 1-shot	LST+MAP	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network	See all
CIFAR-FS 5-way (1-shot)	LST+MAP	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network	See all
OMNIGLOT - 1-Shot, 5-way	MC2+	Meta-Curvature	See all
CUB 200 5-way 5-shot	LST+MAP	Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network	See all
OMNIGLOT - 5-Shot, 5-way	DCN6-E	Decoder Choice Network for Meta-Learning	See all
Mini-ImageNet - 1-Shot Learning	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
FC100 5-way (1-shot)	R2-D2+Task Aug	Task Augmentation by Rotating for Meta-Learning	See all
FC100 5-way (5-shot)	R2-D2+Task Aug	Task Augmentation by Rotating for Meta-Learning	See all
Mini-ImageNet-CUB 5-way (1-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
Mini-Imagenet 20-way (1-shot)	TIM-GD	Transductive Information Maximization For Few-Shot Learning	See all
Mini-Imagenet 20-way (5-shot)	TIM-GD	Transductive Information Maximization For Few-Shot Learning	See all
CUB-200-2011 - 0-Shot	Word CNN-RNN (DS-SJE Embedding)	Learning Deep Representations of Fine-grained Visual Descriptions	See all
Mini-Imagenet 5-way (10-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
Mini-ImageNet-CUB 5-way (5-shot)	PT+MAP	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning	See all
Stanford Dogs 5-way (5-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
Stanford Cars 5-way (1-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
Stanford Cars 5-way (5-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
CUB 200 50-way (0-shot)	Prototypical Networks	Prototypical Networks for Few-shot Learning	See all
CUB-200 - 0-Shot Learning	TAFE-Net	TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning	See all
SUN - 0-Shot	Synthesised Classifier	Synthesized Classifiers for Zero-Shot Learning	See all
ImageNet - 0-Shot	Synthesised Classifier	Synthesized Classifiers for Zero-Shot Learning	See all
Stanford Dogs 5-way (1-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
CIFAR100 5-way (1-shot)	Delta-encoder	Delta-encoder: an effective sample synthesis method for few-shot object recognition	See all
Caltech-256 5-way (1-shot)	UL-Hopfield (ULH)	Unsupervised Learning using Pretrained CNN and Associative Memory Bank	See all
Mini-ImageNet to CUB - 5 shot learning	TIM-GD	Transductive Information Maximization For Few-Shot Learning	See all
Fewshot-CIFAR100 - 1-Shot Learning	pseudo-shots	Pseudo Shots: Few-Shot Learning with Auxiliary Data	See all
Fewshot-CIFAR100 - 5-Shot Learning	pseudo-shots	Pseudo Shots: Few-Shot Learning with Auxiliary Data	See all
CIFAR-FS - 1-Shot Learning	pseudo-shots	Pseudo Shots: Few-Shot Learning with Auxiliary Data	See all
Flowers-102 - 0-Shot	Word CNN-RNN (DS-SJE Embedding)	Learning Deep Representations of Fine-grained Visual Descriptions	See all
AWA - 0-Shot	Synthesised Classifier	Synthesized Classifiers for Zero-Shot Learning	See all
OMNIGLOT-EMNIST 5-way (1-shot)	DKT + BNCosSim	Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	See all
OMNIGLOT-EMNIST 5-way (5-shot)	DKT + BNCosSim	Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels	See all
FC100 5-way (10-shot)	MTL	Meta-Transfer Learning for Few-Shot Learning	See all
OMNIGLOT - 1-Shot, 423 way	APL	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	See all
OMNIGLOT - 5-Shot, 423 way	APL	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	See all
OMNIGLOT - 1-Shot, 1000 way	APL	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	See all
OMNIGLOT - 5-Shot, 1000 way	APL	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module	See all
AWA1 - 0-Shot	TAFE-Net	TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning	See all
AWA2 - 0-Shot	TAFE-Net	TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning	See all
aPY - 0-Shot	TAFE-Net	TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning	See all
mini-ImageNet - 100-Way	GCR	Few-Shot Learning with Global Class Representations	See all
CIFAR-FS - 5-Shot Learning	pseudo-shots	Pseudo Shots: Few-Shot Learning with Auxiliary Data	See all
miniImagenet → CUB (5-way 1-shot)	LaplacianShot	Laplacian Regularized Few-Shot Learning	See all
miniImagenet → CUB (5-way 5-shot)	LaplacianShot	Laplacian Regularized Few-Shot Learning	See all
iNaturalist (227-way multi-shot)	LaplacianShot	Laplacian Regularized Few-Shot Learning	See all
CUB-200-2011 5-way (1-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
CUB-200-2011 5-way (5-shot)	MATANet	Multi-scale Adaptive Task Attention Network for Few-Shot Learning	See all
Mini-Imagenet 10-way (1-shot)	TIM-GD	Transductive Information Maximization For Few-Shot Learning	See all
Mini-Imagenet 10-way (5-shot)	TIM-GD	Transductive Information Maximization For Few-Shot Learning	See all

Subtasks

Generalized Few-Shot Classification

10 benchmarks

1 papers with code

Datasets

CUB-200-2011

tieredImageNet

miniImageNet

Omniglot

CIFAR-FS

See all 15 datasets

Greatest papers with code

Learning to Remember Rare Events

9 Mar 2017 • tensorflow/models •

We present a large-scale life-long memory module for use in deep learning.

Ranked #8 on Few-Shot Image Classification on OMNIGLOT - 5-Shot, 5-way

FEW-SHOT IMAGE CLASSIFICATION MACHINE TRANSLATION OMNIGLOT ONE-SHOT LEARNING

68,794

Paper
Code

Meta-Learning without Memorization

ICLR 2020 • google-research/google-research •

If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.

Ranked #18 on Few-Shot Image Classification on OMNIGLOT - 5-Shot, 20-way

FEW-SHOT IMAGE CLASSIFICATION

16,048

Paper
Code

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

ICML 2017 • cbfinn/maml •

We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including classification, regression, and reinforcement learning.

Ranked #2 on Few-Shot Image Classification on OMNIGLOT - 5-Shot, 5-way

FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT REGRESSION ONE-SHOT LEARNING

1,815

Paper
Code

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

ICLR 2020 • learnables/learn2learn •

We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

FEW-SHOT IMAGE CLASSIFICATION

1,167

Paper
Code

On First-Order Meta-Learning Algorithms

8 Mar 2018 • learnables/learn2learn •

This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.

Ranked #3 on Image Classification on Tiered ImageNet 5-way (5-shot)

FEW-SHOT IMAGE CLASSIFICATION

1,167

Paper
Code

Prototypical Networks for Few-shot Learning

NeurIPS 2017 • learnables/learn2learn •

We propose prototypical networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each new class.

Ranked #1 on Few-Shot Image Classification on CUB 200 50-way (0-shot)

FEW-SHOT IMAGE CLASSIFICATION ONE-SHOT LEARNING ZERO-SHOT LEARNING

1,167

Paper
Code

Learning to Compare: Relation Network for Few-Shot Learning

CVPR 2018 • floodsung/LearningToCompare_FSL •

Once trained, a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network.

Ranked #2 on Image Classification on Tiered ImageNet 5-way (5-shot)

FEW-SHOT IMAGE CLASSIFICATION ZERO-SHOT LEARNING

831

Paper
Code