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
Grounded Language Learning Fast and Slow
Recent work has shown that large text-based neural language models, trained with conventional supervised learning objectives, acquire a surprising propensity for few- and one-shot learning.
Detecting Hate Speech with GPT-3
Given this capacity, we are interested in whether large language models can be used to identify hate speech and classify text as sexist or racist.
One-shot learning of paired association navigation with biologically plausible schemas
mgkumar138/Oneshot_Reservoir
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But how schemas, conceptualized at Marr's computational level, correspond with neural implementations remains poorly understood, and a biologically plausible computational model of the rodent learning has not been demonstrated.
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
Amgao/RLS-RTMDNet
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CVPR 2020
In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training.
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN.
An In-Depth Evaluation of Federated Learning on Biomedical Natural Language Processing
Language models (LMs) such as BERT and GPT have revolutionized natural language processing (NLP).
Learning to learn with backpropagation of Hebbian plasticity
As a result, the networks "learn how to learn" in order to solve the problem at hand: the trained networks automatically perform fast learning of unpredictable environmental features during their lifetime, expanding the range of solvable problems.
Attentive Recurrent Comparators
gitabcworld/ConvArc
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ICML 2017
Rapid learning requires flexible representations to quickly adopt to new evidence.
Make SVM great again with Siamese kernel for few-shot learning
tilkb/siamese-kernel-machine
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ICLR 2018
While deep neural networks have shown outstanding results in a wide range of applications, learning from a very limited number of examples is still a challenging task.
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm.
