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 3191–3200 of 10307 papers

Title	Date	Tasks	Status	Hype
Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model	Jun 5, 2023	Cross-Lingual TransferLanguage Modeling	—Unverified	0
Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphs	Jun 5, 2023	Entity AlignmentKnowledge Distillation	CodeCode Available	0
Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation	Jun 4, 2023	Image SegmentationIncremental Learning	CodeCode Available	1
Commonsense Knowledge Transfer for Pre-trained Language Models	Jun 4, 2023	Language ModelingLanguage Modelling	—Unverified	0
An Improved Model for Diabetic Retinopathy Detection by using Transfer Learning and Ensemble Learning	Jun 3, 2023	Data AugmentationDiabetic Retinopathy Detection	—Unverified	0
TIES-Merging: Resolving Interference When Merging Models	Jun 2, 2023	Transfer Learning	CodeCode Available	2
Transfer learning for atomistic simulations using GNNs and kernel mean embeddings	Jun 2, 2023	Transfer Learning	CodeCode Available	1
Context selectivity with dynamic availability enables lifelong continual learning	Jun 2, 2023	Continual LearningLifelong learning	CodeCode Available	0
Speech Translation with Foundation Models and Optimal Transport: UPC at IWSLT23	Jun 2, 2023	Knowledge DistillationMachine Translation	—Unverified	0
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy	Jun 2, 2023	SpecificityTransfer Learning	—Unverified	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