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 1781–1790 of 10307 papers

Title	Date	Tasks	Status	Score
Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks	May 14, 2022	Transfer Learning	CodeCode Available	5
HistoKT: Cross Knowledge Transfer in Computational Pathology	Jan 27, 2022	Transfer Learning	CodeCode Available	5
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning	May 7, 2024	regressionscientific discovery	CodeCode Available	5
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data	Mar 7, 2022	ManagementTransfer Learning	CodeCode Available	5
Histopathologic Cancer Detection	Nov 13, 2023	Data AugmentationPrognosis	CodeCode Available	5
Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN	May 20, 2024	Generative Adversarial NetworkImage Segmentation	CodeCode Available	5
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks	Oct 2, 2024	In-Context LearningTransfer Learning	CodeCode Available	5
Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods	Sep 3, 2022	Breast Cancer DetectionTransfer Learning	CodeCode Available	5
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self Attention	Feb 11, 2022	Sentiment AnalysisTransfer Learning	CodeCode Available	5
Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks	Oct 19, 2017	Image SegmentationSemantic Segmentation	CodeCode Available	5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