Few-Shot Image Classification

94 papers with code • 65 benchmarks • 17 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 )

Greatest papers with code

Meta-Learning without Memorization

google-research/google-research ICLR 2020

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 Meta-Learning

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.

Few-Shot Image Classification Few-shot Regression +2

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML

learnables/learn2learn ICLR 2020

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

learnables/learn2learn 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.

Few-Shot Image Classification

Prototypical Networks for Few-shot Learning

learnables/learn2learn 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.

Few-Shot Image Classification General Classification +2

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.

Few-Shot Image Classification Zero-Shot Learning

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.

Few-Shot Image Classification Language Modelling +2

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

google-research/meta-dataset NeurIPS 2019

We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta-learning and few-shot learning literature.

Active Learning Continual Learning +3