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methodology-4

One-Shot Learning

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in one-shot-learning-9

Trend

Dataset

Best Model

Actions

MNIST

MNIST

Libraries

i

Use these libraries to find one-shot-learning-9 models and implementations

learnables/learn2learn

2 papers 2,616

Datasets

MNIST

TACO

TopLogo-10

MatSim

Subtasks

No subtasks available.

Most implemented papers

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

Chelsea Finn, P. Abbeel, S. Levine•Wed Mar 08 2017

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 is proposed.

13278

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

sicara/easy-few-shot-learning

2 papers 1,017

0

Prototypical Networks for Few-shot Learning

R. Zemel, Kevin Swersky, Jake Snell•Tue Mar 14 2017

This work proposes Prototypical Networks for few-shot classification, and provides an analysis showing that some simple design decisions can yield substantial improvements over recent approaches involving complicated architectural choices and meta-learning.

9429 0

Matching Networks for One Shot Learning

O. Vinyals, K. Kavukcuoglu, C. Blundell, Daan Wierstra, T. Lillicrap•Sun Jun 12 2016

This work employs ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories to learn a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types.

7946 0

One-shot Learning with Memory-Augmented Neural Networks

Adam Santoro, Daan Wierstra, M. Botvinick, T. Lillicrap, Sergey Bartunov•Wed May 18 2016

The ability of a memory-augmented neural network to rapidly assimilate new data, and leverage this data to make accurate predictions after only a few samples is demonstrated.

536 0

One-Shot Learning for Semantic Segmentation

Irfan Essa, Byron Boots, Amirreza Shaban, Shray Bansal, Z. Liu•Thu Aug 31 2017

This work trains a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN), and uses this FCN to perform dense pixel-level prediction on a test image for the new semantic class.

832 0

The Omniglot challenge: a 3-year progress report

R. Salakhutdinov, B. Lake, J. Tenenbaum•Fri Feb 08 2019

It is concluded that recent approaches are still far from human-like concept learning on Omniglot, a challenge that requires performing many tasks with a single model.

144 0

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

V. Lempitsky, Egor Zakharov, Aliaksandra Shysheya, Egor Burkov•Sun May 19 2019

This work presents a system that performs lengthy meta-learning on a large dataset of videos, and is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators.

673 0

Dynamic Few-Shot Visual Learning Without Forgetting

N. Komodakis, Spyros Gidaris•Tue Apr 24 2018

This work proposes to extend an object recognition system with an attention based few-shot classification weight generator, and to redesign the classifier of a ConvNet model as the cosine similarity function between feature representations and classification weight vectors.

1190 0

One-Shot Instance Segmentation

M. Bethge, Claudio Michaelis, Alexander S. Ecker, Ivan Ustyuzhaninov•Tue Nov 27 2018

Siamese Mask R-CNN is extended by a Siamese backbone encoding both reference image and scene, allowing it to target detection and segmentation towards the reference category.

96 0

Adding a benchmark result helps the community track progress.

One-Shot Learning | State-of-the-Art