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 3731–3740 of 10307 papers

Title	Date	Tasks	Status
Federated learning: Applications, challenges and future directions	May 18, 2022	Federated LearningMedical Diagnosis	—Unverified
Explicit Induction Bias for Transfer Learning with Convolutional Networks	Jan 1, 2018	Transfer Learning	—Unverified
Explicit Knowledge Transfer for Weakly-Supervised Code Generation	Nov 30, 2022	Code GenerationFew-Shot Learning	—Unverified
Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?	Mar 25, 2024	Transfer Learning	—Unverified
Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation	Nov 25, 2021	Domain AdaptationTransfer Learning	—Unverified
Exploiting CNNs for Semantic Segmentation with Pascal VOC	Apr 26, 2023	Data AugmentationSegmentation	—Unverified
Exploiting Convolutional Representations for Multiscale Human Settlement Detection	Jul 18, 2017	Transfer Learning	—Unverified
Exploiting Convolution Filter Patterns for Transfer Learning	Aug 23, 2017	Transfer Learning	—Unverified
Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification	Dec 22, 2018	ClassificationCross-Lingual Document Classification	—Unverified
Federated Learning for Spoken Language Understanding	Dec 1, 2020	Federated LearningIntent Detection	—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