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 2301–2310 of 10307 papers

Title	Date	Tasks	Status
Tuned Inception V3 for Recognizing States of Cooking Ingredients	May 5, 2019	Transfer Learning	—Unverified
Brain Tumor Classification on MRI in Light of Molecular Markers	Sep 29, 2024	Brain Tumor ClassificationDiagnostic	—Unverified
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0	Dec 21, 2023	Speech SynthesisTransfer Learning	—Unverified
An Information-Theoretic Approach to Transferability in Task Transfer Learning	Dec 20, 2022	Model SelectionTransfer Learning	—Unverified
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach	May 12, 2024	SegmentationTransfer Learning	—Unverified
On the Generalization for Transfer Learning: An Information-Theoretic Analysis	Jul 12, 2022	Domain AdaptationTransfer Learning	—Unverified
Deepfake Detection of Face Images based on a Convolutional Neural Network	Mar 14, 2025	DeepFake DetectionFace Swapping	—Unverified
Brain-mediated Transfer Learning of Convolutional Neural Networks	May 24, 2019	BIG-bench Machine LearningTransfer Learning	—Unverified
Brain informed transfer learning for categorizing construction hazards	Nov 17, 2022	Brain Computer InterfaceEEG	—Unverified
An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation	May 11, 2020	Brain Tumor SegmentationDomain Adaptation	—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