Computer Vision
 
Edit

Few-Shot Image Classification

201 papers with code • 88 benchmarks • 23 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

Subtasks

Most implemented papers

Most implemented Social Latest No code

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.

82
Paper
Code

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.

58
Paper
Code

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
Paper
Code

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
Paper
Code

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.

13
Paper
Code

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.

13
Paper
Code

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.

13
Paper
Code

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
Paper
Code

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.

9
Paper
Code

How to train your MAML

AntreasAntoniou/HowToTrainYourMAMLPytorch • • ICLR 2019

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

9
Paper
Code
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy