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 4181–4190 of 10307 papers

Title	Date	Tasks	Status	Hype
Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre Classification	Oct 14, 2022	Action RecognitionClassification	CodeCode Available	0
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI	Oct 14, 2022	DiagnosticTransfer Learning	CodeCode Available	0
NSCGCN: A novel deep GCN model to diagnosis COVID-19	Oct 13, 2022	Computed Tomography (CT)COVID-19 Diagnosis	—Unverified	0
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling	Oct 13, 2022	Multi-Task LearningSentence	—Unverified	0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets	Oct 13, 2022	Few-Shot LearningMeta-Learning	—Unverified	0
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources	Oct 13, 2022	Deep Reinforcement LearningScheduling	—Unverified	0
Unified Vision and Language Prompt Learning	Oct 13, 2022	Domain GeneralizationFew-Shot Learning	CodeCode Available	1
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation	Oct 12, 2022	Data AugmentationTransfer Learning	CodeCode Available	0
Federated Continual Learning for Text Classification via Selective Inter-client Transfer	Oct 12, 2022	Continual LearningFederated Learning	CodeCode Available	0
Self-supervised video pretraining yields robust and more human-aligned visual representations	Oct 12, 2022	Contrastive Learningobject-detection	—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