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 3941–3950 of 10307 papers

Title	Date	Tasks	Status	Hype
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning	Dec 2, 2022	Federated LearningMeta-Learning	—Unverified	0
Video-based Pose-Estimation Data as Source for Transfer Learning in Human Activity Recognition	Dec 2, 2022	Activity RecognitionHuman Activity Recognition	—Unverified	0
Geometry-Aware Network for Domain Adaptive Semantic Segmentation	Dec 2, 2022	Depth EstimationDepth Prediction	—Unverified	0
Quantum median filter for Total Variation image denoising	Dec 2, 2022	DenoisingImage Denoising	—Unverified	0
Rethinking Two Consensuses of the Transferability in Deep Learning	Dec 1, 2022	Deep LearningGeneral Knowledge	—Unverified	0
Finetune like you pretrain: Improved finetuning of zero-shot vision models	Dec 1, 2022	DescriptiveFew-Shot Learning	CodeCode Available	1
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling	Nov 30, 2022	ClusteringEnsemble Learning	—Unverified	0
Explicit Knowledge Transfer for Weakly-Supervised Code Generation	Nov 30, 2022	Code GenerationFew-Shot Learning	—Unverified	0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks	Nov 30, 2022	Action RecognitionDomain Adaptation	—Unverified	0
ClaRet -- A CNN Architecture for Optical Coherence Tomography	Nov 30, 2022	Transfer 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