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 2881–2890 of 10307 papers

Title	Date	Tasks	Status	Hype
Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid Algorithm with Transformer and CNN Encoders	Aug 26, 2023	Few-Shot LearningImage Segmentation	CodeCode Available	1
Privacy-Enhanced Zero-Shot Learning via Data-Free Knowledge Transfer	Aug 25, 2023	Transfer LearningZero-Shot Learning	CodeCode Available	0
An Ensemble Approach to Personalized Real Time Predictive Writing for Experts	Aug 25, 2023	Language ModellingLarge Language Model	—Unverified	0
CEIMVEN: An Approach of Cutting Edge Implementation of Modified Versions of EfficientNet (V1-V2) Architecture for Breast Cancer Detection and Classification from Ultrasound Images	Aug 25, 2023	Breast Cancer DetectionTransfer Learning	CodeCode Available	0
Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere	Aug 25, 2023	CPUData Augmentation	—Unverified	0
Mesh-Wise Prediction of Demographic Composition from Satellite Images Using Multi-Head Convolutional Neural Network	Aug 25, 2023	Transfer Learning	—Unverified	0
Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan	Aug 25, 2023	Clinical KnowledgeMortality Prediction	—Unverified	0
Heterogeneous Federated Learning via Personalized Generative Networks	Aug 25, 2023	Federated LearningTransfer Learning	—Unverified	0
RestNet: Boosting Cross-Domain Few-Shot Segmentation with Residual Transformation Network	Aug 25, 2023	Cross-Domain Few-ShotSemantic Segmentation	CodeCode Available	1
Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem	Aug 24, 2023	Combinatorial OptimizationTransfer 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