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One-Shot Learning

30 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.

( Image credit: Siamese Neural Networks for One-shot Image Recognition )

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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 FEW-SHOT REGRESSION 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.

OMNIGLOT ONE-SHOT LEARNING

Prototypical Networks for Few-shot Learning

NeurIPS 2017 learnables/learn2learn

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

Matching Networks for One Shot Learning

NeurIPS 2016 oscarknagg/few-shot

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 OMNIGLOT ONE-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

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

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

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.

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

Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification

ICCV 2019 OasisYang/SSG

Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i. e. the number of independent identities from the target domain is unknown).

ONE-SHOT LEARNING UNSUPERVISED DOMAIN ADAPTATION UNSUPERVISED PERSON RE-IDENTIFICATION