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 4061–4070 of 10307 papers

Title	Date	Tasks	Status	Hype
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations	Nov 7, 2022	ClusteringRecommendation Systems	CodeCode Available	0
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling	Nov 7, 2022	DisentanglementDomain Generalization	CodeCode Available	1
Enabling Deep Learning-based Physical-layer Secret Key Generation for FDD-OFDM Systems in Multi-Environments	Nov 6, 2022	Meta-LearningTransfer Learning	—Unverified	0
Rotation-equivariant Graph Neural Networks for Learning Glassy Liquids Representations	Nov 6, 2022	Transfer LearningTranslation	—Unverified	0
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting	Nov 6, 2022	Motion ForecastingMotion Style Transfer	CodeCode Available	1
1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems	Nov 5, 2022	Event DetectionFault Detection	—Unverified	0
A Data-Driven Evolutionary Transfer Optimization for Expensive Problems in Dynamic Environments	Nov 5, 2022	Transfer Learning	—Unverified	0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion	Nov 4, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models	Nov 4, 2022	Genre classificationKeyword Spotting	CodeCode Available	0
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment	Nov 4, 2022	Linear evaluationPseudo Label	CodeCode Available	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