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 1051–1075 of 3073 papers

TitleStatusHype
Active Learning for Video Classification with Frame Level Queries—0
Active Learning in Physics: From 101, to Progress, and Perspective—0
Training Ensembles with Inliers and Outliers for Semi-supervised Active LearningCode0
For Women, Life, Freedom: A Participatory AI-Based Social Web Analysis of a Watershed Moment in Iran's Gender Struggles—0
Understanding Uncertainty SamplingCode0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation—0
REAL: A Representative Error-Driven Approach for Active LearningCode0
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data—0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
Revisiting Sample Size Determination in Natural Language UnderstandingCode0
Ticket-BERT: Labeling Incident Management Tickets with Language Models—0
Thompson sampling for improved exploration in GFlowNets—0
PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and ClassificationCode0
Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions—0
Large Language Models as Annotators: Enhancing Generalization of NLP Models at Minimal Cost—0
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
Multi-Task Consistency for Active Learning—0
Annotation Cost Efficient Active Learning for Content Based Image Retrieval—0
Taming Small-sample Bias in Low-budget Active Learning—0
Perturbation-Based Two-Stage Multi-Domain Active Learning—0
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images—0
Amortized Inference for Gaussian Process Hyperparameters of Structured KernelsCode0
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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