# One-Shot Learning

69 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 )

## Libraries

Use these libraries to find One-Shot Learning models and implementations
2 papers
1,923

# Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

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.

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# Prototypical Networks for Few-shot Learning

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.

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# Matching Networks for One Shot Learning

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.

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# One-shot Learning with Memory-Augmented Neural Networks

19 May 2016

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

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# Siamese neural networks for one-shot image recognition

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.

9

# The Omniglot challenge: a 3-year progress report

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.

7

In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.

7

# One-Shot Learning for Semantic Segmentation

11 Sep 2017

Low-shot learning methods for image classification support learning from sparse data.

6

# Dynamic Few-Shot Visual Learning without Forgetting

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).

4

# 'Less Than One'-Shot Learning: Learning N Classes From M<N Samples

17 Sep 2020

We propose the `less than one'-shot learning task where models must learn $N$ new classes given only $M<N$ examples and we show that this is achievable with the help of soft labels.

4