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Few-Shot Image Classification

139 papers with code • 75 benchmarks • 19 datasets

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 )

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

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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.

69
Paper
Code

Prototypical Networks for Few-shot Learning

oscarknagg/few-shot • • 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.

40
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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.

22
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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.

21
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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.

13
Paper
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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.

10
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.

9
Paper
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How to train your MAML

AntreasAntoniou/HowToTrainYourMAMLPytorch • • ICLR 2019

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

8
Paper
Code

Meta-Learning with Differentiable Convex Optimization

kjunelee/MetaOptNet • • CVPR 2019

We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.

6
Paper
Code

Towards a Neural Statistician

conormdurkan/neural-statistician • • 7 Jun 2016

We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.

5
Paper
Code
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