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 3861–3870 of 10307 papers

Title	Date	Tasks	Status
Faces of Experimental Pain: Transferability of Deep Learned Heat Pain Features to Electrical Pain	Jun 17, 2024	Transfer Learning	—Unverified
Facial Action Unit Recognition Based on Transfer Learning	Mar 25, 2022	Facial Action Unit DetectionTransfer Learning	—Unverified
Facial Anatomical Landmark Detection using Regularized Transfer Learning with Application to Fetal Alcohol Syndrome Recognition	Sep 12, 2021	Anatomical Landmark DetectionFacial Landmark Detection	—Unverified
Combining Behaviors with the Successor Features Keyboard	Oct 24, 2023	Transfer Learning	—Unverified
Combining Convolution and Recursive Neural Networks for Sentiment Analysis	Jan 27, 2018	SentenceSentiment Analysis	—Unverified
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning	Feb 27, 2022	Lip ReadingTransfer Learning	—Unverified
A Framework of Meta Functional Learning for Regularising Knowledge Transfer	Mar 28, 2022	Cross-Domain Few-Shotcross-domain few-shot learning	—Unverified
Facial Landmark Correlation Analysis	Nov 24, 2019	Facial Landmark DetectionFew-Shot Learning	—Unverified
Using LLMs to Establish Implicit User Sentiment of Software Desirability	Aug 2, 2024	Sentiment AnalysisTransfer Learning	—Unverified
Decomposition-Based Transfer Distance Metric Learning for Image Classification	Apr 8, 2019	ClassificationGeneral Classification	—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