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Few-Shot Image Classification

67 papers with code · Methodology

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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Greatest papers with 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.

FEW-SHOT IMAGE CLASSIFICATION

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.

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

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

On First-Order Meta-Learning Algorithms

8 Mar 2018learnables/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.

FEW-SHOT IMAGE CLASSIFICATION

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.

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

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.

FEW-SHOT IMAGE CLASSIFICATION ZERO-SHOT LEARNING

A Closer Look at Few-shot Classification

ICLR 2019 wyharveychen/CloserLookFewShot

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

DOMAIN GENERALIZATION FEW-SHOT IMAGE CLASSIFICATION

A Closer Look at Few-shot Classification

ICLR 2019 wyharveychen/CloserLookFewShot

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

DOMAIN GENERALIZATION FEW-SHOT IMAGE CLASSIFICATION

Matching Networks for One Shot Learning

NeurIPS 2016 oscarknagg/few-shot

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.

FEW-SHOT IMAGE CLASSIFICATION LANGUAGE MODELLING METRIC LEARNING OMNIGLOT ONE-SHOT LEARNING