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 3871–3880 of 10307 papers

Title	Date	Tasks	Status	Hype
ProgNet: A Transferable Deep Network for Aircraft Engine Damage Propagation Prognosis under Real Flight Conditions	Dec 23, 2022	DenoisingDimensionality Reduction	CodeCode Available	0
Federated Learning -- Methods, Applications and beyond	Dec 22, 2022	Federated LearningTransfer Learning	—Unverified	0
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task	Dec 21, 2022	Model CompressionTransfer Learning	CodeCode Available	0
Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime	Dec 21, 2022	Fault DetectionObject	—Unverified	0
MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning	Dec 21, 2022	SensitivityTransfer Learning	CodeCode Available	1
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning	Dec 21, 2022	Few-Shot LearningLanguage Modelling	—Unverified	0
Using Machine Learning to Determine Morphologies of z<1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey	Dec 20, 2022	Transfer Learning	CodeCode Available	0
A Framework of Customer Review Analysis Using the Aspect-Based Opinion Mining Approach	Dec 20, 2022	Aspect ExtractionOpinion Mining	—Unverified	0
HyperBO+: Pre-training a universal prior for Bayesian optimization with hierarchical Gaussian processes	Dec 20, 2022	Bayesian OptimizationGaussian Processes	CodeCode Available	0
An Information-Theoretic Approach to Transferability in Task Transfer Learning	Dec 20, 2022	Model SelectionTransfer Learning	—Unverified	0

Show:10 25 50

← PrevPage 388 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