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 2631–2640 of 10307 papers

Title	Date	Tasks	Status	Hype
DREAM+: Efficient Dataset Distillation by Bidirectional Representative Matching	Oct 23, 2023	Dataset DistillationTransfer Learning	CodeCode Available	1
Unlocking the Transferability of Tokens in Deep Models for Tabular Data	Oct 23, 2023	Transfer Learning	—Unverified	0
Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition	Oct 22, 2023	Activity RecognitionHuman Activity Recognition	—Unverified	0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning	Oct 22, 2023	Edge-computingExplainable artificial intelligence	—Unverified	0
Minimax Optimal Transfer Learning for Kernel-based Nonparametric Regression	Oct 21, 2023	regressionTransfer Learning	—Unverified	0
On the Transferability of Visually Grounded PCFGs	Oct 21, 2023	Transfer Learning	CodeCode Available	1
Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder for Image Translation of Dotted Arabic Expiration Dates	Oct 21, 2023	Transfer LearningTranslation	—Unverified	0
Foundation Model's Embedded Representations May Detect Distribution Shift	Oct 20, 2023	Sentiment AnalysisSentiment Classification	—Unverified	0
Using Human-like Mechanism to Weaken Effect of Pre-training Weight Bias in Face-Recognition Convolutional Neural Network	Oct 20, 2023	Face RecognitionTransfer Learning	—Unverified	0
A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis	Oct 20, 2023	Fault DiagnosisTransfer Learning	—Unverified	0

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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