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 2491–2500 of 10307 papers

Title	Date	Tasks	Status
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification	Apr 26, 2018	ClassificationGeneral Classification	—Unverified
An Empirical Study of Scaling Laws for Transfer	Aug 30, 2024	Transfer Learning	—Unverified
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation	Feb 25, 2022	ManagementTraffic Prediction	—Unverified
Deep Face Recognition Model Compression via Knowledge Transfer and Distillation	Jun 3, 2019	Face RecognitionKnowledge Distillation	—Unverified
Deep transfer learning for improving single-EEG arousal detection	Apr 10, 2020	EEGElectroencephalogram (EEG)	—Unverified
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition	Sep 8, 2017	Transfer Learning	—Unverified
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings	Jul 6, 2019	Transfer Learning	—Unverified
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation	Nov 1, 2017	Machine TranslationTransfer Learning	—Unverified
A Deep Learning Approach for Diabetic Retinopathy detection using Transfer Learning	Jan 1, 2021	Diabetic Retinopathy DetectionMedical Diagnosis	—Unverified
Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison	Mar 27, 2022	ArticlesFew-Shot Object Detection	—Unverified

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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