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 1021–1030 of 3073 papers

TitleStatusHype
MoBYv2AL: Self-supervised Active Learning for Image ClassificationCode1
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking—0
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Hybrid Active Learning via Deep Clustering for Video Action Detection—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Label-Efficient Interactive Time-Series Anomaly Detection—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