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 3771–3780 of 10307 papers

Title	Date	Tasks	Status
FedNILM: Applying Federated Learning to NILM Applications at the Edge	Jun 7, 2021	Federated LearningModel Compression	—Unverified
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning	Dec 18, 2020	intent-classificationIntent Classification	—Unverified
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer	May 4, 2024	class-incremental learningClass Incremental Learning	—Unverified
FEED: Feature-level Ensemble Effect for knowledge Distillation	May 1, 2019	Knowledge DistillationTransfer Learning	—Unverified
Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features	Sep 18, 2022	Age-Invariant Face RecognitionClassification	—Unverified
Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective	Jul 12, 2024	Transfer Learning	—Unverified
Coefficient Shape Transfer Learning for Functional Linear Regression	Jun 13, 2025	Nutritionregression	—Unverified
A Survey of Fish Tracking Techniques Based on Computer Vision	Oct 6, 2021	Image EnhancementTransfer Learning	—Unverified
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains	Oct 30, 2018	PredictionTransfer Learning	—Unverified
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning	Jul 25, 2023	Activity RecognitionFederated 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