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 1341–1350 of 3073 papers

Title	Date	Tasks	Status
Difficult Cases: From Data to Learning, and Back	Jun 1, 2014	Active LearningDependency Parsing	—Unverified
Active Learning Guided Fine-Tuning for enhancing Self-Supervised Based Multi-Label Classification of Remote Sensing Images	Jun 12, 2023	Active LearningMulti-Label Classification	—Unverified
Diffusion Active Learning: Towards Data-Driven Experimental Design in Computed Tomography	Apr 4, 2025	Active LearningComputed Tomography (CT)	—Unverified
Diffusion-based Deep Active Learning	Mar 23, 2020	Active Learning	—Unverified
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation	Jan 7, 2021	Active LearningBIG-bench Machine Learning	—Unverified
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning	Dec 31, 2022	Active LearningAttribute	—Unverified
Distribution-Dependent Sample Complexity of Large Margin Learning	Apr 5, 2012	Active LearningGeneral Classification	—Unverified
DIRECT: Deep Active Learning under Imbalance and Label Noise	Dec 14, 2023	Active Learning	—Unverified
Confidence Adjusted Surprise Measure for Active Resourceful Trials (CA-SMART): A Data-driven Active Learning Framework for Accelerating Material Discovery under Resource Constraints	Mar 27, 2025	Active LearningBayesian Optimization	—Unverified
AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization	Apr 21, 2021	Active LearningBayesian Optimization	—Unverified

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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