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 3611–3620 of 10307 papers

Title	Date	Tasks	Status
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas	Dec 19, 2023	Point Cloud SegmentationSegmentation	—Unverified
Domain adaption and physical constrains transfer learning for shale gas production	Dec 18, 2023	DiversityDomain Adaptation	—Unverified
Federated Multi-View Synthesizing for Metaverse	Dec 18, 2023	Domain AdaptationFederated Learning	—Unverified
Social Learning: Towards Collaborative Learning with Large Language Models	Dec 18, 2023	MemorizationTransfer Learning	—Unverified
LaViP:Language-Grounded Visual Prompts	Dec 18, 2023	Few-Shot LearningTransfer Learning	—Unverified
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System	Dec 18, 2023	Transfer Learning	—Unverified
Semantic Segmentation Using Transfer Learning on Fisheye Images	Dec 17, 2023	Image SegmentationSegmentation	—Unverified
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation	Dec 17, 2023	DeepFake DetectionDomain Adaptation	—Unverified
p-Laplacian Adaptation for Generative Pre-trained Vision-Language Models	Dec 17, 2023	Image CaptioningQuestion Answering	CodeCode Available
Cross-Domain Robustness of Transformer-based Keyphrase Generation	Dec 17, 2023	Abstractive Text SummarizationKeyphrase Extraction	—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