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 351–375 of 3073 papers

TitleStatusHype
Pseudo-triplet Guided Few-shot Composed Image Retrieval—0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool—0
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts—0
Markerless Multi-view 3D Human Pose Estimation: a survey—0
Automated Progressive Red TeamingCode0
Scoping Review of Active Learning Strategies and their Evaluation Environments for Entity Recognition TasksCode0
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method—0
CALICO: Confident Active Learning with Integrated Calibration—0
Cost-Effective Proxy Reward Model Construction with On-Policy and Active Learning—0
DCoM: Active Learning for All LearnersCode2
Physics-informed active learning with simultaneous weak-form latent space dynamics identification—0
Assistive Image Annotation Systems with Deep Learning and Natural Language Capabilities: A Review—0
Towards Deep Active Learning in Avian Bioacoustics—0
The Use of AI-Robotic Systems for Scientific Discovery—0
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial OptimizationCode0
Active Learning for Fair and Stable Online Allocations—0
Generative AI for Enhancing Active Learning in Education: A Comparative Study of GPT-3.5 and GPT-4 in Crafting Customized Test Questions—0
Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center DatasetCode1
Large-Scale Dataset Pruning in Adversarial Training through Data Importance ExtrapolationCode0
Towards Bayesian Data Selection—0
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic SegmentationCode1
Enhancing Text Classification through LLM-Driven Active Learning and Human AnnotationCode0
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification—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