About

One-shot learning is the task of learning information about object categories from a single training example.

( Image credit: Siamese Neural Networks for One-shot Image Recognition )

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Datasets

Greatest papers with code

Grounded Language Learning Fast and Slow

ICLR 2021 deepmind/lab

Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.

META-LEARNING ONE-SHOT LEARNING

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.

CLASSIFICATION FEW-SHOT IMAGE CLASSIFICATION FEW-SHOT REGRESSION ONE-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.

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

The Omniglot challenge: a 3-year progress report

9 Feb 2019brendenlake/omniglot

Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks.

CLASSIFICATION OMNIGLOT ONE-SHOT LEARNING

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

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).

CLASSIFICATION FEW-SHOT IMAGE CLASSIFICATION OBJECT RECOGNITION ONE-SHOT LEARNING

One-shot Learning with Memory-Augmented Neural Networks

19 May 2016tristandeleu/ntm-one-shot

Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning."

ONE-SHOT LEARNING