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 4231–4240 of 10307 papers

Title	Date	Tasks	Status	Hype
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks	Oct 3, 2022	Texture ClassificationTransfer Learning	—Unverified	0
Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond	Oct 3, 2022	Electromyography (EMG)Survey	—Unverified	0
HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization	Oct 3, 2022	Bayesian OptimizationTransfer Learning	—Unverified	0
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data	Oct 3, 2022	Brain Computer InterfaceDomain Adaptation	—Unverified	0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning	Oct 3, 2022	Cross-Lingual TransferDomain Adaptation	—Unverified	0
An Analysis of RF Transfer Learning Behavior Using Synthetic Data	Oct 3, 2022	Domain AdaptationTransfer Learning	—Unverified	0
Unbiased Scene Graph Generation using Predicate Similarities	Oct 3, 2022	DescriptiveGraph Generation	—Unverified	0
Towards a Unified View on Visual Parameter-Efficient Transfer Learning	Oct 3, 2022	Action RecognitionImage Classification	CodeCode Available	1
Visual Prompt Tuning for Generative Transfer Learning	Oct 3, 2022	Image GenerationTransfer Learning	CodeCode Available	1
Under the Cover Infant Pose Estimation using Multimodal Data	Oct 3, 2022	Pose EstimationTransfer Learning	CodeCode Available	0

Show:10 25 50

← PrevPage 424 of 1031Next →

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