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 891900 of 3073 papers

TitleStatusHype
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors0
Benchmarking Multi-Domain Active Learning on Image Classification0
Towards Comparable Active Learning0
Active Foundational Models for Fault Diagnosis of Electrical Motors0
Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection0
The Battleship Approach to the Low Resource Entity Matching ProblemCode0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond0
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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