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 51–60 of 10307 papers

Title	Date	Tasks	Status	Hype
Auto-Compressing Networks	Jun 11, 2025	Transfer Learning	—Unverified	0
An Effective End-to-End Solution for Multimodal Action Recognition	Jun 11, 2025	Action RecognitionComputational Efficiency	—Unverified	0
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach	Jun 11, 2025	Community DetectionFairness	CodeCode Available	0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation	Jun 10, 2025	Anomaly DetectionData Augmentation	—Unverified	0
Efficient Learning of Vehicle Controller Parameters via Multi-Fidelity Bayesian Optimization: From Simulation to Experiment	Jun 10, 2025	Bayesian OptimizationTransfer Learning	—Unverified	0
Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL)	Jun 10, 2025	Autonomous DrivingReinforcement Learning (RL)	—Unverified	0
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective	Jun 10, 2025	Data AugmentationDeep Learning	—Unverified	0
SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation	Jun 10, 2025	Data AugmentationImage Segmentation	CodeCode Available	0
Do MIL Models Transfer?	Jun 10, 2025	Multiple Instance LearningTransfer Learning	CodeCode Available	2
Variational Supervised Contrastive Learning	Jun 9, 2025	Contrastive LearningFew-Shot 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