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 1081–1090 of 10307 papers

Title	Date	Tasks	Status	Hype
A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classification	Apr 1, 2021	General ClassificationTransfer Learning	CodeCode Available	1
MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering Models	Apr 1, 2021	Information RetrievalQuestion Answering	CodeCode Available	1
AmbiFC: Fact-Checking Ambiguous Claims with Evidence	Apr 1, 2021	Claim VerificationEvidence Selection	CodeCode Available	1
Going deeper with Image Transformers	Mar 31, 2021	image-classificationImage Classification	CodeCode Available	1
Deep Image Harmonization by Bridging the Reality Gap	Mar 31, 2021	Image HarmonizationTransfer Learning	CodeCode Available	1
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data	Mar 30, 2021	Change DetectionSelf-Supervised Learning	CodeCode Available	1
SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification	Mar 30, 2021	ClassificationGeneral Classification	CodeCode Available	1
Multi-Disease Detection in Retinal Imaging based on Ensembling Heterogeneous Deep Learning Models	Mar 26, 2021	Disease PredictionEnsemble Learning	CodeCode Available	1
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning	Mar 26, 2021	Point Cloud RegistrationTransfer Learning	CodeCode Available	1
Dynamic Domain Adaptation for Efficient Inference	Mar 26, 2021	Domain AdaptationDomain Generalization	CodeCode Available	1

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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