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 4131–4140 of 10307 papers

Title	Date	Tasks	Status
Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem	Jul 13, 2024	BenchmarkingDeep Learning	—Unverified
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification	Feb 3, 2021	Decision MakingSpeaker Verification	—Unverified
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction	Apr 9, 2025	Multi-Task LearningProperty Prediction	—Unverified
Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning	Nov 28, 2022	ManagementTransfer Learning	—Unverified
GenFighter: A Generative and Evolutive Textual Attack Removal	Apr 17, 2024	Adversarial RobustnessTransfer Learning	—Unverified
Geometric Framework for Cell Oversegmentation	Feb 3, 2025	Binary ClassificationCell Segmentation	—Unverified
Data-Free Knowledge Transfer: A Survey	Dec 31, 2021	Data-free Knowledge DistillationDomain Adaptation	—Unverified
Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks	Mar 4, 2021	BlockingData Augmentation	—Unverified
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory	May 22, 2024	class-incremental learningClass Incremental Learning	—Unverified
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation	Mar 8, 2025	Federated Learningimage-classification	—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