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 4631–4640 of 10307 papers

Title	Date	Tasks	Status	Hype
Longitudinal detection of new MS lesions using Deep Learning	Jun 16, 2022	Data AugmentationDeep Learning	—Unverified	0
Multi scale Feature Extraction and Fusion for Online Knowledge Distillation	Jun 16, 2022	Knowledge DistillationTransfer Learning	—Unverified	0
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation	Jun 16, 2022	Graph Neural NetworkRepresentation Learning	CodeCode Available	1
A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT	Jun 15, 2022	PrognosisTransfer Learning	—Unverified	0
Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification	Jun 15, 2022	Transfer Learning	CodeCode Available	1
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification	Jun 15, 2022	Few-Shot Learningimage-classification	—Unverified	0
Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks	Jun 15, 2022	Super-ResolutionTransfer Learning	—Unverified	0
Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness Characterization from Optical Spectra of Perovskite Thin Films	Jun 14, 2022	Transfer Learning	—Unverified	0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks	Jun 14, 2022	Knowledge Distillationreinforcement-learning	—Unverified	0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models	Jun 14, 2022	Cross-Lingual TransferDiagnostic	—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