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 2331–2340 of 10307 papers

Title	Date	Tasks	Status	Hype
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning	Dec 29, 2023	Fault DiagnosisTransfer Learning	—Unverified	0
Weed mapping in multispectral drone imagery using lightweight vision transformers	Dec 28, 2023	ManagementSemantic Segmentation	CodeCode Available	1
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach	Dec 28, 2023	Multimodal Sentiment AnalysisSentiment Analysis	—Unverified	0
Any-point Trajectory Modeling for Policy Learning	Dec 28, 2023	Trajectory ModelingTransfer Learning	CodeCode Available	2
OmniDialog: An Omnipotent Pre-training Model for Task-Oriented Dialogue System	Dec 28, 2023	Dialogue GenerationDialogue Management	—Unverified	0
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data	Dec 27, 2023	Imitation LearningTransfer Learning	—Unverified	0
Transfer and Alignment Network for Generalized Category Discovery	Dec 27, 2023	AttributeRepresentation Learning	CodeCode Available	0
Soft Contrastive Learning for Time Series	Dec 27, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection	Dec 27, 2023	Few-Shot Object Detectionobject-detection	—Unverified	0
Recursive Distillation for Open-Set Distributed Robot Localization	Dec 26, 2023	Continual LearningImage Retrieval	—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