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 321–330 of 3073 papers

TitleStatusHype
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms—0
Active Learning for WBAN-based Health Monitoring—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Bayesian Active Learning for Semantic Segmentation—0
Active Learning for Neural PDE SolversCode5
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
A Cross-Domain Benchmark for Active LearningCode0
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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