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 2161–2170 of 10307 papers

Title	Date	Tasks	Status	Score
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains	Jul 2, 2025	Atari GamesChatbot	CodeCode Available	5
Generalization Through The Lens Of Leave-One-Out Error	Mar 7, 2022	Generalization BoundsTransfer Learning	CodeCode Available	5
Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer	Dec 1, 2019	AttributeMulti-Task Learning	CodeCode Available	5
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture	May 22, 2023	Active LearningDecision Making	CodeCode Available	5
Generalizable Local Feature Pre-training for Deformable Shape Analysis	Mar 27, 2023	Transfer Learning	CodeCode Available	5
Beyond Knowledge Silos: Task Fingerprinting for Democratization of Medical Imaging AI	Dec 11, 2024	Medical Image AnalysisMulti-Task Learning	CodeCode Available	5
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation	May 12, 2023	Domain AdaptationForm	CodeCode Available	5
Context selectivity with dynamic availability enables lifelong continual learning	Jun 2, 2023	Continual LearningLifelong learning	CodeCode Available	5
GAN Cocktail: mixing GANs without dataset access	Jun 7, 2021	Transfer Learning	CodeCode Available	5
GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection	May 21, 2021	Transfer Learning	CodeCode Available	5

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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