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Few-Shot Learning

155 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 FEW-SHOT REGRESSION ONE-SHOT LEARNING

Language Models are Few-Shot Learners

28 May 2020openai/gpt-3

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

 SOTA for Language Modelling on Penn Treebank (Word Level) (using extra training data)

COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION

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

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning

31 Jul 2017learnables/learn2learn

In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial.

FEW-SHOT LEARNING

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

Big Transfer (BiT): General Visual Representation Learning

24 Dec 2019google-research/big_transfer

We conduct detailed analysis of the main components that lead to high transfer performance.

 SOTA for Image Classification on ObjectNet (Bounding Box) (using extra training data)

FEW-SHOT LEARNING FINE-GRAINED IMAGE CLASSIFICATION REPRESENTATION LEARNING