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 2311–2320 of 10307 papers

Title	Date	Tasks	Status
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
Brain2Model Transfer: Training sensory and decision models with human neural activity as a teacher	Jun 25, 2025	Autonomous DrivingDecision Making	—Unverified
Abnormal Event Detection in Urban Surveillance Videos Using GAN and Transfer Learning	Nov 19, 2020	Event DetectionOptical Flow Estimation	—Unverified
Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition	Feb 17, 2023	Cross-corpusEmotion Recognition	—Unverified
Deep Learning and Health Informatics for Smart Monitoring and Diagnosis	Aug 5, 2022	Data AugmentationDeep Learning	—Unverified
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks	May 20, 2015	Transfer Learning	—Unverified
Towards Complementary Knowledge Distillation for Efficient Dense Image Prediction	Jan 24, 2024	Implicit RelationsInstance Segmentation	—Unverified
An Improvement for Capsule Networks using Depthwise Separable Convolution	Jul 30, 2020	Deep LearningTransfer Learning	—Unverified
Deep Feature Learning for Graphs	Apr 28, 2017	Graph Representation LearningRepresentation Learning	—Unverified
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review	Jul 3, 2020	Decision Makingreinforcement-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