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 821–830 of 3073 papers

TitleStatusHype
BatchGFN: Generative Flow Networks for Batch Active LearningCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
Multi-Task Consistency for Active Learning—0
Multi-Fidelity Active Learning with GFlowNetsCode2
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Taming Small-sample Bias in Low-budget Active Learning—0
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images—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