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 8190 of 3073 papers

TitleStatusHype
Hierarchical Point-based Active Learning for Semi-supervised Point Cloud Semantic SegmentationCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Divide and Adapt: Active Domain Adaptation via Customized LearningCode1
ProbVLM: Probabilistic Adapter for Frozen Vision-Language ModelsCode1
M-VAAL: Multimodal Variational Adversarial Active Learning for Downstream Medical Image Analysis TasksCode1
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient LearningCode1
ActiveGLAE: A Benchmark for Deep Active Learning with TransformersCode1
Towards Balanced Active Learning for Multimodal ClassificationCode1
Memory-Based Dual Gaussian Processes for Sequential LearningCode1
infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-informationCode1
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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