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 1711–1720 of 10307 papers

Title	Date	Tasks	Status
Adversarial Vulnerability of Active Transfer Learning	Jan 26, 2021	Active LearningData Poisoning	—Unverified
Active Learning of Ordinal Embeddings: A User Study on Football Data	Jul 26, 2022	Active LearningInformation Retrieval	—Unverified
Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization	Jun 23, 2024	Decision MakingSequential Decision Making	—Unverified
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning	Oct 1, 2021	ClassificationData Augmentation	—Unverified
A predictive physics-aware hybrid reduced order model for reacting flows	Jan 24, 2023	Dimensionality ReductionTransfer Learning	—Unverified
Adversarial Transfer of Pose Estimation Regression	Jun 20, 2020	Camera Pose EstimationDomain Adaptation	—Unverified
Adversarial Transfer Learning for Punctuation Restoration	Apr 1, 2020	Language ModelingLanguage Modelling	—Unverified
A Practical Guide to Fine-tuning Language Models with Limited Data	Nov 14, 2024	Few-Shot LearningTransfer Learning	—Unverified
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates	Jan 20, 2021	Active LearningTransfer Learning	—Unverified
A Practical Approach towards Causality Mining in Clinical Text using Active Transfer Learning	Dec 10, 2020	Decision MakingTransfer 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