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 51–75 of 3073 papers

TitleStatusHype
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
A deep active learning system for species identification and counting in camera trap imagesCode1
Active Statistical InferenceCode1
Active Learning for Domain Adaptation: An Energy-Based ApproachCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
A Comparative Survey of Deep Active LearningCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model EvaluationCode1
Active Prompt Learning in Vision Language ModelsCode1
Active Learning by Feature MixingCode1
Active Learning at the ImageNet ScaleCode1
Active Sensing for Communications by LearningCode1
Active Testing: Sample-Efficient Model EvaluationCode1
Active Imitation Learning with Noisy GuidanceCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Anomaly Detection via EnsemblesCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Active learning with MaskAL reduces annotation effort for training Mask R-CNNCode1
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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