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 4651–4660 of 10307 papers

Title	Date	Tasks	Status	Hype
PAC-Net: A Model Pruning Approach to Inductive Transfer Learning	Jun 12, 2022	Transfer Learning	—Unverified	0
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking	Jun 12, 2022	Animal Pose EstimationDomain Generalization	CodeCode Available	1
Toward Real-world Single Image Deraining: A New Benchmark and Beyond	Jun 11, 2022	Domain GeneralizationImage Restoration	CodeCode Available	1
Toward Dynamic Stability Assessment of Power Grid Topologies using Graph Neural Networks	Jun 10, 2022	Transfer Learning	CodeCode Available	0
A Correlation-Ratio Transfer Learning and Variational Stein's Paradox	Jun 10, 2022	Transfer Learning	—Unverified	0
Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning	Jun 10, 2022	Transfer Learning	—Unverified	0
On Hypothesis Transfer Learning of Functional Linear Models	Jun 9, 2022	regressionTransfer Learning	—Unverified	0
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization	Jun 9, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training	Jun 9, 2022	Transfer Learning	CodeCode Available	0
The Missing Link: Finding label relations across datasets	Jun 9, 2022	SpecificityTransfer 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