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 326–350 of 3073 papers

Title	Date	Tasks	Status	Score
Crowd Counting With Partial Annotations in an Image	Jan 1, 2021	Active LearningCrowd Counting	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
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter	Jun 15, 2023	Active Learning	CodeCode Available	5
Cross-context News Corpus for Protest Events related Knowledge Base Construction	Aug 1, 2020	Active LearningArticles	CodeCode Available	5
CrudeOilNews: An Annotated Crude Oil News Corpus for Event Extraction	Apr 8, 2022	Active LearningArticles	CodeCode Available	5
Active Few-Shot Learning with FASL	Apr 20, 2022	Active LearningFew-Shot Learning	CodeCode Available	5
covEcho Resource constrained lung ultrasound image analysis tool for faster triaging and active learning	Jun 21, 2022	Active LearningGPU	CodeCode Available	5
Cost-Sensitive Active Learning for Incomplete Data	Jun 23, 2022	Active LearningAttribute	CodeCode Available	5
Cost-Sensitive Reference Pair Encoding for Multi-Label Learning	Nov 29, 2016	Active LearningMulti-Label Classification	CodeCode Available	5
Cost-Accuracy Aware Adaptive Labeling for Active Learning	May 24, 2021	Active LearningInformativeness	CodeCode Available	5
Active Learning for Entity Filtering in Microblog Streams	Aug 1, 2015	Active Learning	CodeCode Available	5
Cost-Effective Active Learning for Deep Image Classification	Jan 13, 2017	Active LearningClassification	CodeCode Available	5
Active Learning for Entity Alignment	Jan 24, 2020	Active LearningEntity Alignment	CodeCode Available	5
Cooperative Inverse Reinforcement Learning	Jun 9, 2016	Active Learningreinforcement-learning	CodeCode Available	5
Correlation Clustering with Adaptive Similarity Queries	May 28, 2019	Active LearningClustering	CodeCode Available	5
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