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 2001–2025 of 3073 papers

TitleStatusHype
Wireless for Machine Learning—0
A Survey of Deep Active LearningCode0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneCode1
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
Active learning of deep surrogates for PDEs: Application to metasurface design—0
Probabilistic Deep Learning for Instance Segmentation—0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper—0
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound ImagesCode0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Contextual Diversity for Active LearningCode1
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision—0
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification—0
Cross-Model Image Annotation Platform with Active Learning—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—0
Active Classification with Uncertainty Comparison QueriesCode0
Dual Adversarial Network for Deep Active Learning—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Two Stream Active Query Suggestion for Active Learning in Connectomics—0
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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