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 17011710 of 3073 papers

TitleStatusHype
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities0
Visual Causal Feature Learning0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis0
Visual Supervision in Bootstrapped Information Extraction0
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas0
VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning0
Weakly Supervised Active Learning with Cluster Annotation0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning0
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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