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One-Shot Learning

19 papers with code · Methodology
Subtask of Few-Shot Learning

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

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Greatest papers with code

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.

FEW-SHOT IMAGE CLASSIFICATION ONE-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.

ONE-SHOT LEARNING

Prototypical Networks for Few-shot Learning

NeurIPS 2017 orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

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 ONE-SHOT LEARNING ZERO-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

Siamese neural networks for one-shot image recognition

ICML deep learning workshop, vol. 2. 2015. 2015 sorenbouma/keras-oneshot

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

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).

FEW-SHOT IMAGE CLASSIFICATION OBJECT RECOGNITION ONE-SHOT LEARNING

Matching Networks for One Shot Learning

NeurIPS 2016 AntreasAntoniou/MatchingNetworks

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 ONE-SHOT LEARNING

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

20 May 2019grey-eye/talking-heads

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

META-LEARNING ONE-SHOT LEARNING TALKING HEAD GENERATION

One-Shot Instance Segmentation

28 Nov 2018bethgelab/siamese-mask-rcnn

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.

ONE-SHOT INSTANCE SEGMENTATION ONE-SHOT LEARNING ONE-SHOT OBJECT DETECTION

One-Shot Learning for Semantic Segmentation

11 Sep 2017lzzcd001/OSLSM

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

IMAGE CLASSIFICATION ONE-SHOT LEARNING ONE-SHOT SEGMENTATION