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 1721–1730 of 10307 papers

Title	Date	Tasks	Status
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning	Dec 10, 2020	Decision MakingTransfer Learning	—Unverified
Approximation by non-symmetric networks for cross-domain learning	May 6, 2023	Transfer LearningTranslation	—Unverified
Active Learning for Rumor Identification on Social Media	Nov 1, 2021	Active LearningTransfer Learning	—Unverified
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning	Oct 1, 2017	DenoisingDimensionality Reduction	—Unverified
Approximated Prompt Tuning for Vision-Language Pre-trained Models	Jun 27, 2023	image-classificationImage Classification	—Unverified
FlowBERT: Prompt-tuned BERT for variable flow field prediction	May 20, 2025	Dimensionality ReductionFew-Shot Learning	—Unverified
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology	Oct 16, 2019	Transfer Learning	—Unverified
Considerations for a PAP Smear Image Analysis System with CNN Features	Jun 23, 2018	ClassificationGeneral Classification	—Unverified
Constrained Deep Transfer Feature Learning and its Applications	Sep 23, 2017	Facial Expression RecognitionFacial Expression Recognition (FER)	—Unverified
Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks	Nov 18, 2020	Emotion RecognitionFacial Expression Recognition	—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