Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1701–1710 of 10307 papers

Title	Date	Tasks	Status
A Probabilistic Approach to Knowledge Translation	Jul 12, 2015	Transfer LearningTranslation	—Unverified
A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation	Mar 6, 2024	Contrastive LearningDecoder	—Unverified
Adverse Drug Reaction Detection in Twitter Using RoBERTa and Rules	Dec 1, 2020	Task 2Transfer Learning	—Unverified
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis	Feb 3, 2025	Contrastive LearningDisentanglement	—Unverified
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors	Nov 19, 2022	Generative Adversarial NetworkPrivacy Preserving	—Unverified
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms	Nov 2, 2023	Autonomous DrivingAutonomous Vehicles	—Unverified
Active Learning with Transfer Learning	Jul 1, 2012	Active LearningDomain Adaptation	—Unverified
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors	Sep 30, 2021	Brain Tumor SegmentationSegmentation	—Unverified
A Primer on Pretrained Multilingual Language Models	Jul 1, 2021	Joint Multilingual Sentence RepresentationsMultilingual text classification	—Unverified
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition	Apr 8, 2020	Gesture RecognitionTransfer Learning	—Unverified

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All datasets Office-Home 100 sleep nights of 8 caregivers BanglaLekha Isolated Dataset COCO70 KITTI Object Tracking Evaluation 2012 Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	APCLIP	Accuracy	84.2	—	Unverified
2	DFA-ENT	Accuracy	69.2	—	Unverified
3	DFA-SAFN	Accuracy	69.1	—	Unverified
4	EasyTL	Accuracy	63.3	—	Unverified
5	MEDA	Accuracy	60.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	10-20% Mask PSNR	3.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Chatterjee, Dutta et al.[1]	Accuracy	96.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Co-Tuning	Accuracy	85.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Physical Access	EER	5.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	riadd.aucmedi	AUROC	0.95	—	Unverified