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 176–200 of 3073 papers

TitleStatusHype
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
Class-Balanced Active Learning for Image ClassificationCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
A Comparative Survey of Deep Active LearningCode1
Code-free development and deployment of deep segmentation models for digital pathologyCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Contextual Diversity for Active LearningCode1
Counting People by Estimating People FlowsCode1
Creating Custom Event Data Without Dictionaries: A Bag-of-TricksCode1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
Active Learning Meets Optimized Item SelectionCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
Deep Active Learning for Named Entity RecognitionCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for BERT: An Empirical StudyCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
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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