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 126–150 of 3073 papers

TitleStatusHype
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Stochastic Batch Acquisition: A Simple Baseline for Deep Active LearningCode1
A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled SamplesCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
AL-GTD: Deep Active Learning for Gaze Target DetectionCode1
AISecKG: Knowledge Graph Dataset for Cybersecurity EducationCode1
A-LINK: Recognizing Disguised Faces via Active Learning based Inter-Domain KnowledgeCode1
Active Learning for Convolutional Neural Networks: A Core-Set ApproachCode1
Active Learning for Computationally Efficient Distribution of Binary Evolution SimulationsCode1
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
A Framework and Benchmark for Deep Batch Active Learning for RegressionCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation EffortsCode1
All you need are a few pixels: semantic segmentation with PixelPickCode1
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
A Mathematical Analysis of Learning Loss for Active Learning in RegressionCode1
An Informative Path Planning Framework for Active Learning in UAV-based Semantic MappingCode1
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active LearningCode1
A Simple Baseline for Low-Budget Active LearningCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Active Anomaly Detection via EnsemblesCode1
A deep active learning system for species identification and counting in camera trap 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