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

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Showing 4791–4800 of 10307 papers

Title	Date	Tasks	Status
Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies	Dec 3, 2018	Transfer Learning	—Unverified
Learning to Win Lottery Tickets in BERT Transfer via Task-agnostic Mask Training	Dec 17, 2021	Transfer Learning	—Unverified
Learning Transferability in Deep Segmentation of Liver Metastases	Aug 22, 2024	Transfer Learning	—Unverified
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation	Oct 12, 2023	Decision MakingDomain Adaptation	—Unverified
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models	Jul 16, 2018	ClassificationDomain Adaptation	—Unverified
Learning Transferable Feature Representations Using Neural Networks	Jul 1, 2019	Transfer Learning	—Unverified
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling	Oct 22, 2020	Transfer Learning	—Unverified
Learning Transfers over Several Programming Languages	Oct 25, 2023	Cross-Lingual TransferIn-Context Learning	—Unverified
Learning ULMFiT and Self-Distillation with Calibration for Medical Dialogue System	Jul 20, 2021	Decision MakingKnowledge Distillation	—Unverified
Learning unbiased features	Dec 17, 2014	Domain AdaptationRepresentation 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