One-Shot Learning
93 papers with code • 1 benchmarks • 4 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 )
Benchmarks
These leaderboards are used to track progress in One-Shot Learning
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Libraries
Use these libraries to find One-Shot Learning models and implementations
MNIST
TACO
TopLogo-10
MatSim
Most implemented papers
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
cbfinn/maml
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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.
Prototypical Networks for Few-shot Learning
jakesnell/prototypical-networks
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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.
Matching Networks for One Shot Learning
oscarknagg/few-shot
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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.
One-shot Learning with Memory-Augmented Neural Networks
Despite recent breakthroughs in the applications of deep neural networks, one setting that presents a persistent challenge is that of "one-shot learning."
Siamese neural networks for one-shot image recognition
tensorfreitas/Siamese-Networks-for-One-Shot-Learning
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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.
One-Shot Learning for Semantic Segmentation
Low-shot learning methods for image classification support learning from sparse data.
The Omniglot challenge: a 3-year progress report
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.
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
vincent-thevenin/Realistic-Neural-Talking-Head-Models
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ICCV 2019
In order to create a personalized talking head model, these works require training on a large dataset of images of a single person.
Dynamic Few-Shot Visual Learning without Forgetting
gidariss/FewShotWithoutForgetting
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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).
One-Shot Instance Segmentation
bethgelab/siamese-mask-rcnn
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We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult.
