Computer Vision
 
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Few-Shot Image Classification

187 papers with code • 76 benchmarks • 21 datasets

Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically < 6 examples). The goal is to enable models to recognize and classify new images with minimal supervision and limited data, without having to train on large datasets. (typically < 6 examples)

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

Libraries

Use these libraries to find Few-Shot Image Classification models and implementations

Datasets

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Most implemented papers

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

cbfinn/maml • • ICML 2017

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.

79
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Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP • • 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

46
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Prototypical Networks for Few-shot Learning

jakesnell/prototypical-networks • • NeurIPS 2017

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.

42
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Matching Networks for One Shot Learning

oscarknagg/few-shot • • NeurIPS 2016

Our algorithm improves one-shot accuracy on ImageNet from 87. 6% to 93. 2% and from 88. 0% to 93. 8% on Omniglot compared to competing approaches.

26
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A Closer Look at Few-shot Classification

wyharveychen/CloserLookFewShot • • ICLR 2019

Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples.

13
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Learning to Compare: Relation Network for Few-Shot Learning

floodsung/LearningToCompare_FSL • • CVPR 2018

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.

12
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On First-Order Meta-Learning Algorithms

openai/supervised-reptile • • 8 Mar 2018

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.

12
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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

google-research/meta-dataset • • ICLR 2020

Few-shot classification refers to learning a classifier for new classes given only a few examples.

12
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How to train your MAML

AntreasAntoniou/HowToTrainYourMAMLPytorch • • ICLR 2019

The field of few-shot learning has recently seen substantial advancements.

9
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Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks

juho-lee/set_transformer • • 1 Oct 2018

Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances.

8
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Code
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