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 4141–4150 of 10307 papers

Title	Date	Tasks	Status
Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation	Mar 8, 2025	Federated Learningimage-classification	—Unverified
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement	Dec 11, 2021	Transfer Learning	—Unverified
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning	May 23, 2023	Photoplethysmography (PPG)Transfer Learning	—Unverified
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks	May 8, 2022	Generative Adversarial NetworkGraph Classification	—Unverified
Adaptive Trajectory Prediction via Transferable GNN	Mar 9, 2022	Autonomous DrivingGraph Neural Network	—Unverified
Data-Efficient Strategies for Probabilistic Voltage Envelopes under Network Contingencies	Oct 1, 2023	Transfer Learning	—Unverified
Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning	Nov 29, 2022	Transfer Learning	—Unverified
Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation.	Jul 1, 2022	Machine TranslationTransfer Learning	—Unverified
Data-Efficient Methods for Dialogue Systems	Dec 5, 2020	Anomaly DetectionData Augmentation	—Unverified
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection	Jun 27, 2018	Active LearningTransfer 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