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 2551–2560 of 10307 papers

Title	Date	Tasks	Status	Score
Few-Shot Image Recognition With Knowledge Transfer	Oct 1, 2019	Transfer Learning	CodeCode Available	5
FedRef: Communication-Efficient Bayesian Fine Tuning with Reference Model	Jun 29, 2025	Brain Tumor SegmentationFederated Learning	CodeCode Available	5
A Systematic Comparison of Architectures for Document-Level Sentiment Classification	Feb 19, 2020	ClassificationDocument Classification	CodeCode Available	5
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning	Dec 21, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available	5
Federated Machine Learning: Concept and Applications	Feb 13, 2019	BIG-bench Machine LearningFederated Learning	CodeCode Available	5
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation	Sep 5, 2024	Contrastive LearningFederated Learning	CodeCode Available	5
Decoding Neural Responses in Mouse Visual Cortex through a Deep Neural Network	Oct 26, 2019	Transfer Learning	CodeCode Available	5
Cross-project Defect Prediction with An Enhanced Transfer Boosting Algorithm	Nov 29, 2022	PredictionTransfer Learning	CodeCode Available	5
Federated Domain Generalization via Prompt Learning and Aggregation	Nov 15, 2024	Domain GeneralizationPrivacy Preserving	CodeCode Available	5
Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application	Jul 24, 2021	Federated LearningMulti-Task Learning	CodeCode Available	5

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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