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

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Showing 2851–2860 of 10307 papers

Title	Date	Tasks	Status
Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting	Sep 5, 2023	Deep LearningEconometrics	CodeCode Available
Graph Self-Contrast Representation Learning	Sep 5, 2023	Contrastive LearningGraph Representation Learning	—Unverified
Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach	Sep 5, 2023	image-classificationImage Classification	—Unverified
A Survey of the Impact of Self-Supervised Pretraining for Diagnostic Tasks with Radiological Images	Sep 5, 2023	Clinical KnowledgeDiagnostic	—Unverified
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained Models	Sep 4, 2023	Reinforcement Learning (RL)Transfer Learning	CodeCode Available
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras	Sep 4, 2023	Keypoint DetectionPose Estimation	—Unverified
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets	Sep 4, 2023	BenchmarkingMotor Imagery	CodeCode Available
Active flow control for three-dimensional cylinders through deep reinforcement learning	Sep 4, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors	Sep 4, 2023	Transfer Learning	—Unverified
User lung cancer classification using efficientnet from ct scan images	Sep 3, 2023	Cancer ClassificationComputed Tomography (CT)	—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