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 4401–4410 of 10307 papers

Title	Date	Tasks	Status	Hype
RRWaveNet: A Compact End-to-End Multi-Scale Residual CNN for Robust PPG Respiratory Rate Estimation	Aug 18, 2022	Feature EngineeringPhotoplethysmography (PPG)	—Unverified	0
Disentangled Contrastive Learning for Social Recommendation	Aug 18, 2022	Contrastive LearningRepresentation Learning	CodeCode Available	0
LAMA-Net: Unsupervised Domain Adaptation via Latent Alignment and Manifold Learning for RUL Prediction	Aug 17, 2022	DecoderDomain Adaptation	—Unverified	0
Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning	Aug 17, 2022	SegmentationTransfer Learning	CodeCode Available	0
Path Planning of Cleaning Robot with Reinforcement Learning	Aug 17, 2022	reinforcement-learningReinforcement Learning	—Unverified	0
Grapes disease detection using transfer learning	Aug 16, 2022	Transfer Learning	—Unverified	0
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets	Aug 15, 2022	Transfer Learning	CodeCode Available	1
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks	Aug 14, 2022	Few-Shot LearningNode Classification	CodeCode Available	1
A Theory for Knowledge Transfer in Continual Learning	Aug 14, 2022	Continual LearningTransfer Learning	—Unverified	0
Teacher Guided Training: An Efficient Framework for Knowledge Transfer	Aug 14, 2022	Generalization Boundsimage-classification	—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