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 10511060 of 3073 papers

TitleStatusHype
Active Learning for Video Classification with Frame Level Queries0
Active Learning in Physics: From 101, to Progress, and Perspective0
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 Struggles0
Active Learning with Contrastive Pre-training for Facial Expression RecognitionCode0
Understanding Uncertainty SamplingCode0
Optimal and Efficient Binary Questioning for Human-in-the-Loop Annotation0
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data0
Human in the AI loop via xAI and Active Learning for Visual Inspection0
REAL: A Representative Error-Driven Approach for Active LearningCode0
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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