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 301–325 of 3073 papers

TitleStatusHype
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experimentsCode1
Fink: early supernovae Ia classification using active learningCode1
Active Bayesian Causal InferenceCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryCode1
HUMAN: Hierarchical Universal Modular ANnotatorCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
Neural Networks Are More Productive Teachers Than Human Raters: Active Mixup for Data-Efficient Knowledge Distillation from a Blackbox ModelCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Correlation Clustering with Adaptive Similarity QueriesCode0
Cooperative Inverse Reinforcement LearningCode0
Cost-Accuracy Aware Adaptive Labeling for Active LearningCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Active Generation for Image ClassificationCode0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Continual egocentric object recognitionCode0
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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