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 401–425 of 3073 papers

Title	Date	Tasks	Status	Score
Cost-Effective Active Learning for Melanoma Segmentation	Nov 24, 2017	Active LearningImage Segmentation	CodeCode Available	5
Cost-Effective Active Learning for Deep Image Classification	Jan 13, 2017	Active LearningClassification	CodeCode Available	5
Cost Effective Active Search	Dec 1, 2019	Active Learning	CodeCode Available	5
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach	Jul 18, 2018	Active LearningBIG-bench Machine Learning	CodeCode Available	5
Active Learning on Neural Networks through Interactive Generation of Digit Patterns and Visual Representation	Oct 2, 2023	Active Learning	CodeCode Available	5
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter	Jun 15, 2023	Active Learning	CodeCode Available	5
Correlation Clustering with Adaptive Similarity Queries	May 28, 2019	Active LearningClustering	CodeCode Available	5
Cost-Accuracy Aware Adaptive Labeling for Active Learning	May 24, 2021	Active LearningInformativeness	CodeCode Available	5
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria	Jun 30, 2018	Active Learningobject-detection	CodeCode Available	5
Controllable Textual Inversion for Personalized Text-to-Image Generation	Apr 11, 2023	Active LearningImage Generation	CodeCode Available	5
Conversational Disease Diagnosis via External Planner-Controlled Large Language Models	Apr 4, 2024	Active LearningDecision Making	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
Continual egocentric object recognition	Dec 6, 2019	Active LearningNovelty Detection	CodeCode Available	5
Cooperative Inverse Reinforcement Learning	Jun 9, 2016	Active Learningreinforcement-learning	CodeCode Available	5
Cost-Sensitive Active Learning for Incomplete Data	Jun 23, 2022	Active LearningAttribute	CodeCode Available	5
Constraining the Parameters of High-Dimensional Models with Active Learning	May 19, 2019	Active LearningAstronomy	CodeCode Available	5
Context Selection and Rewriting for Video-based Educational Question Generation	Apr 28, 2025	Active LearningQuestion Generation	CodeCode Available	5
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation	Nov 6, 2024	Active LearningBayesian Optimization	CodeCode Available	5
Active Learning of Spin Network Models	Mar 25, 2019	Active Learning	CodeCode Available	5
Confidence Estimation Using Unlabeled Data	Jul 19, 2023	Active Learningimage-classification	CodeCode Available	5
A3: Active Adversarial Alignment for Source-Free Domain Adaptation	Sep 27, 2024	Active LearningDomain Adaptation	CodeCode Available	5
Active learning via informed search in movement parameter space for efficient robot task learning and transfer	Feb 21, 2019	Active Learning	CodeCode Available	5
Confidence-Aware Active Feedback for Interactive Instance Search	Oct 23, 2021	Active LearningInstance Search	CodeCode Available	5
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics	Nov 25, 2021	Active LearningAge Estimation	CodeCode Available	5
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning	Nov 29, 2016	Active LearningMulti-Label Classification	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