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 3901–3910 of 10307 papers

Title	Date	Tasks	Status	Hype
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model	Dec 14, 2022	image-classificationImage Classification	CodeCode Available	1
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes	Dec 13, 2022	DiagnosticTransfer Learning	—Unverified	0
Improving generalization in reinforcement learning through forked agents	Dec 13, 2022	reinforcement-learningReinforcement Learning	—Unverified	0
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining	Dec 13, 2022	PositionPrivacy Preserving	CodeCode Available	1
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision	Dec 12, 2022	Active LearningTransfer Learning	—Unverified	0
Synthetic Image Data for Deep Learning	Dec 12, 2022	Deep Learningimage-classification	—Unverified	0
Transfer Learning using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes	Dec 12, 2022	Transfer Learning	CodeCode Available	0
Evolutionary Multitasking with Solution Space Cutting for Point Cloud Registration	Dec 12, 2022	Point Cloud RegistrationTransfer Learning	—Unverified	0
Towards Practical Plug-and-Play Diffusion Models	Dec 12, 2022	Depth EstimationImage Generation	CodeCode Available	1
Resolving Semantic Confusions for Improved Zero-Shot Detection	Dec 12, 2022	Generalized Zero-Shot Object DetectionObject Detection	CodeCode Available	1

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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