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 371380 of 3073 papers

TitleStatusHype
Continual egocentric object recognitionCode0
Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral ImagesCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Cost-Effective Active Learning for Deep Image ClassificationCode0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
Constraining the Parameters of High-Dimensional Models with Active LearningCode0
Confidence Estimation Using Unlabeled DataCode0
Context Selection and Rewriting for Video-based Educational Question GenerationCode0
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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