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 2201–2210 of 10307 papers

Title	Date	Tasks	Status
CactusNets: Layer Applicability as a Metric for Transfer Learning	Apr 20, 2018	Transfer Learning	—Unverified
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems	Feb 11, 2021	Anomaly DetectionTime Series Analysis	—Unverified
CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning	Mar 7, 2025	Transfer Learning	—Unverified
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation	Jan 1, 2024	Knowledge DistillationTransfer Learning	—Unverified
An Efficient Approach to Detecting Lung Nodules Using Swin Transformer	Mar 3, 2025	Lung Nodule DetectionTransfer Learning	—Unverified
Data-Efficient Challenges in Visual Inductive Priors: A Retrospective	Jun 10, 2025	Data AugmentationDeep Learning	—Unverified
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning	Nov 11, 2019	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning	Nov 29, 2022	Transfer Learning	—Unverified
Bypassing Optimization Complexity through Transfer Learning & Deep Neural Nets for Speech Intelligibility Improvement	Dec 1, 2021	Transfer Learning	—Unverified
Burgers' pinns with implicit euler transfer learning	Oct 23, 2023	Transfer Learning	—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