One-Shot Learning

57 papers with code • 1 benchmarks • 3 datasets

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 )

Greatest papers with code

Ludwig: a type-based declarative deep learning toolbox

uber/ludwig 17 Sep 2019

In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.

Image Captioning Image Classification +12

Grounded Language Learning Fast and Slow

deepmind/lab ICLR 2021

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.

Grounded language learning Meta-Learning +1

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

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

The Omniglot challenge: a 3-year progress report

brendenlake/omniglot 9 Feb 2019

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.

General Classification One-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

Dynamic Few-Shot Visual Learning without Forgetting

gidariss/FewShotWithoutForgetting CVPR 2018

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 General Classification +2

Siamese neural networks for one-shot image recognition

tensorfreitas/Siamese-Networks-for-One-Shot-Learning ICML deep learning workshop, vol. 2. 2015. 2015

The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available.

 Ranked #1 on One-Shot Learning on MNIST (using extra training data)

One-Shot Learning