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 )


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

Most implemented papers

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.

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.

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.

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.

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.

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.

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.

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.

How to train your MAML

AntreasAntoniou/HowToTrainYourMAMLPytorch ICLR 2019

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

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.