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 101–150 of 3073 papers

TitleStatusHype
A comprehensive survey on deep active learning in medical image analysisCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Class-Balanced Active Learning for Image ClassificationCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them)Code1
Consistency-based Active Learning for Object DetectionCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Active Learning by Acquiring Contrastive ExamplesCode1
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
DEAL: Difficulty-aware Active Learning for Semantic SegmentationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
DeepAL: Deep Active Learning in PythonCode1
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language ModelsCode1
Active WeaSuL: Improving Weak Supervision with Active LearningCode1
Active Learning by Feature MixingCode1
Adaptive Superpixel for Active Learning in Semantic SegmentationCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
Active Testing: Sample-Efficient Model EvaluationCode1
D2ADA: Dynamic Density-aware Active Domain Adaptation for Semantic SegmentationCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
ActiveNeRF: Learning where to See with Uncertainty EstimationCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Meets Optimized Item SelectionCode1
Active Learning on a Budget: Opposite Strategies Suit High and Low BudgetsCode1
Active Learning from the WebCode1
Active Learning Through a Covering LensCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Rethinking the Data Annotation Process for Multi-view 3D Pose Estimation with Active Learning and Self-TrainingCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Sensing for Communications by LearningCode1
Active Statistical InferenceCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Active Anomaly Detection via EnsemblesCode1
Active Pointly-Supervised Instance SegmentationCode1
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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