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Few-Shot Learning

68 papers with code · Methodology

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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 ONE-SHOT LEARNING

On First-Order Meta-Learning Algorithms

8 Mar 2018openai/supervised-reptile

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 FEW-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 FEW-SHOT LEARNING ZERO-SHOT LEARNING

Prototypical Networks for Few-shot Learning

NeurIPS 2017 orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

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

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 FEW-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 FEW-SHOT LEARNING

Large-Scale Long-Tailed Recognition in an Open World

CVPR 2019 zhmiao/OpenLongTailRecognition-OLTR

We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.

FEW-SHOT LEARNING OPEN SET LEARNING

Dynamic Few-Shot Visual Learning without Forgetting

CVPR 2018 gidariss/FewShotWithoutForgetting

In this context, the goal of our work is to devise a few-shot visual learning system that during test time it will be able to efficiently learn novel categories from only a few training data while at the same time it will not forget the initial categories on which it was trained (here called base categories).

FEW-SHOT IMAGE CLASSIFICATION OBJECT RECOGNITION ONE-SHOT LEARNING

Few-Shot Learning with Graph Neural Networks

ICLR 2018 vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

ACTIVE LEARNING FEW-SHOT LEARNING GRAPH NEURAL NETWORK

Few-Shot Learning with Graph Neural Networks

10 Nov 2017vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

ACTIVE LEARNING FEW-SHOT LEARNING GRAPH NEURAL NETWORK