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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 341–350 of 3073 papers

Title	Date	Tasks	Status	Score
Cost-Effective Active Learning for Melanoma Segmentation	Nov 24, 2017	Active LearningImage Segmentation	CodeCode Available	5
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space	Nov 18, 2024	Active Learning	CodeCode Available	5
Controllable Textual Inversion for Personalized Text-to-Image Generation	Apr 11, 2023	Active LearningImage Generation	CodeCode Available	5
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation	Jan 14, 2025	Active Learningclass-incremental learning	CodeCode Available	5
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models	May 9, 2019	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Continual egocentric object recognition	Dec 6, 2019	Active LearningNovelty Detection	CodeCode Available	5
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models	Apr 4, 2024	Active LearningDecision Making	CodeCode Available	5
Cost Effective Active Search	Dec 1, 2019	Active Learning	CodeCode Available	5
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space	Aug 25, 2017	Active Learning	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5

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Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	TypiClust	Accuracy	93.2	—	Unverified
2	PT4AL	Accuracy	93.1	—	Unverified
3	Learning loss	Accuracy	91.01	—	Unverified
4	CoreGCN	Accuracy	90.7	—	Unverified
5	Core-set	Accuracy	89.92	—	Unverified
6	Random Baseline (Resnet18)	Accuracy	88.45	—	Unverified
7	Random Baseline (VGG16)	Accuracy	85.09	—	Unverified