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 2611–2620 of 3073 papers

TitleStatusHype
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Active Learning for Deep Networks: a Margin Based Approach—0
Adversarial Sampling for Active Learning—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—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