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 1801–1810 of 10307 papers

Title	Date	Tasks	Status	Score
CaT: Weakly Supervised Object Detection with Category Transfer	Aug 17, 2021	Objectobject-detection	CodeCode Available	5
Cats or CAT scans: transfer learning from natural or medical image source datasets?	Oct 12, 2018	DiversityMedical Image Analysis	CodeCode Available	5
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models	Nov 4, 2022	Genre classificationKeyword Spotting	CodeCode Available	5
Integrating Curricula with Replays: Its Effects on Continual Learning	Jul 8, 2023	Continual LearningTransfer Learning	CodeCode Available	5
Intensive Care as One Big Sequence Modeling Problem	Feb 27, 2024	Transfer Learning	CodeCode Available	5
Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning	May 22, 2022	Transfer Learning	CodeCode Available	5
Accelerating Malware Classification: A Vision Transformer Solution	Sep 28, 2024	Classificationimage-classification	CodeCode Available	5
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactions	May 18, 2023	Neural Architecture SearchTransfer Learning	CodeCode Available	5
Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification	Jul 13, 2021	image-classificationImage Classification	CodeCode Available	5
Hierarchical transfer learning with applications for electricity load forecasting	Nov 16, 2021	Additive modelsLoad Forecasting	CodeCode Available	5

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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