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 4471–4480 of 10307 papers

Title	Date	Tasks	Status	Hype
Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-Divergence	Jul 21, 2022	ImputationTransfer Learning	CodeCode Available	0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer	Jul 21, 2022	Domain AdaptationFederated Learning	—Unverified	0
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery	Jul 21, 2022	Transfer Learning	—Unverified	0
Sequence Models for Drone vs Bird Classification	Jul 21, 2022	Classificationobject-detection	—Unverified	0
Unsupervised pre-training of graph transformers on patient population graphs	Jul 21, 2022	Language ModelingLanguage Modelling	CodeCode Available	1
What Do We Maximize in Self-Supervised Learning?	Jul 20, 2022	Self-Supervised LearningTransfer Learning	—Unverified	0
Transfer Learning of wav2vec 2.0 for Automatic Lyric Transcription	Jul 20, 2022	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	1
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning	Jul 20, 2022	Feature EngineeringMulti-Task Learning	CodeCode Available	1
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations	Jul 20, 2022	3D Object DetectionAutonomous Driving	—Unverified	0
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson Regression	Jul 19, 2022	regressionTransfer Learning	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