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 3621–3630 of 10307 papers

Title	Date	Tasks	Status	Hype
RePreM: Representation Pre-training with Masked Model for Reinforcement Learning	Mar 3, 2023	Data AugmentationLanguage Modeling	—Unverified	0
Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding	Mar 2, 2023	Speech Synthesistext-to-speech	CodeCode Available	1
Artificial Intelligence for Dementia Research Methods Optimization	Mar 2, 2023	Multi-Task LearningTransfer Learning	—Unverified	0
Heterogeneous Graph Contrastive Learning for Recommendation	Mar 2, 2023	Contrastive LearningRecommendation Systems	CodeCode Available	1
Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots	Mar 2, 2023	Transfer Learning	CodeCode Available	1
Distillation from Heterogeneous Models for Top-K Recommendation	Mar 2, 2023	Knowledge DistillationRecommendation Systems	CodeCode Available	1
Multi-Task Self-Supervised Time-Series Representation Learning	Mar 2, 2023	Anomaly DetectionContrastive Learning	—Unverified	0
Transferring Models Trained on Natural Images to 3D MRI via Position Encoded Slice Models	Mar 2, 2023	Alzheimer's Disease DetectionPosition	CodeCode Available	0
Evidence-empowered Transfer Learning for Alzheimer's Disease	Mar 2, 2023	image-classificationImage Classification	—Unverified	0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning	Mar 2, 2023	Multi-agent Reinforcement Learningreinforcement-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