SOTAVerified

Active Learning

Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model

Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1826–1850 of 3073 papers

TitleStatusHype
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Vulnerability of Active Transfer Learning—0
Online Body Schema Adaptation through Cost-Sensitive Active Learning—0
A Receding Horizon Approach for Simultaneous Active Learning and Control using Gaussian Processes—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image SegmentationCode0
Active Hybrid Classification—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Autonomous synthesis of metastable materials—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Diverse Complexity Measures for Dataset Curation in Self-driving—0
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing—0
Improved active output selection strategy for noisy environments—0
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions—0
Deep Diffusion Processes for Active Learning of Hyperspectral ImagesCode0
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation—0
Active learning for object detection in high-resolution satellite images—0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic—0
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Active Universal Domain Adaptation—0
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels—0
Crowd Counting With Partial Annotations in an ImageCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified