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 3581–3590 of 10307 papers

Title	Date	Tasks	Status	Hype
AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+	Mar 14, 2023	Transfer Learning	CodeCode Available	0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer Learning	Mar 14, 2023	image-classificationImage Classification	CodeCode Available	0
AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks	Mar 14, 2023	AutoMLTransfer Learning	CodeCode Available	0
Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy	Mar 14, 2023	Depression DetectionTransfer Learning	—Unverified	0
DAA: A Delta Age AdaIN operation for age estimation via binary code transformer	Mar 14, 2023	Age EstimationTransfer Learning	CodeCode Available	1
Relational Multi-Task Learning: Modeling Relations between Data and Tasks	Mar 14, 2023	Multi-Task LearningTransfer Learning	CodeCode Available	3
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network	Mar 13, 2023	Cancer ClassificationData Augmentation	—Unverified	0
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach	Mar 13, 2023	Graph ReconstructionManagement	—Unverified	0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering	Mar 13, 2023	Feature EngineeringTransfer Learning	—Unverified	0
Improving physics-informed neural networks with meta-learned optimization	Mar 13, 2023	Transfer Learning	—Unverified	0

Show:10 25 50

← PrevPage 359 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