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 4201–4210 of 10307 papers

Title	Date	Tasks	Status	Hype
Combining datasets to increase the number of samples and improve model fitting	Oct 11, 2022	ImputationTime Series Analysis	CodeCode Available	0
The Fast and Accurate Approach to Detection and Segmentation of Melanoma Skin Cancer using Fine-tuned Yolov3 and SegNet Based on Deep Transfer Learning	Oct 11, 2022	Image SegmentationSegmentation	—Unverified	0
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	Oct 11, 2022	Knowledge DistillationSynthetic Data Generation	CodeCode Available	0
Self-supervised Model Based on Masked Autoencoders Advance CT Scans Classification	Oct 11, 2022	Medical DiagnosisSelf-Supervised Learning	—Unverified	0
Fast Hierarchical Learning for Few-Shot Object Detection	Oct 10, 2022	Few-Shot Object DetectionObject	—Unverified	0
A Survey on Heterogeneous Federated Learning	Oct 10, 2022	Federated LearningSurvey	—Unverified	0
Association Graph Learning for Multi-Task Classification with Category Shifts	Oct 10, 2022	General ClassificationGraph Learning	CodeCode Available	1
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation	Oct 8, 2022	Representation LearningTransfer Learning	CodeCode Available	1
The effect of variable labels on deep learning models trained to predict breast density	Oct 8, 2022	PredictionTransfer Learning	—Unverified	0
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation	Oct 8, 2022	AllTransfer Learning	CodeCode Available	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