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

Showing 28812890 of 3073 papers

TitleStatusHype
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
Word Discriminations for Vocabulary Inventory PredictionCode0
Active Learning for Classifying 2D Grid-Based Level CompletabilityCode0
Learning Structured Representations of Entity Names using Active Learning and Weak SupervisionCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Ranking with Confidence for Large Scale Comparison DataCode0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2Unverified
2PT4ALAccuracy93.1Unverified
3Learning lossAccuracy91.01Unverified
4CoreGCNAccuracy90.7Unverified
5Core-setAccuracy89.92Unverified
6Random Baseline (Resnet18)Accuracy88.45Unverified
7Random Baseline (VGG16)Accuracy85.09Unverified