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 2061–2070 of 10307 papers

Title	Date	Tasks	Status
Cross-Modal Few-Shot Learning: a Generative Transfer Learning Framework	Oct 14, 2024	Few-Shot LearningTransfer Learning	—Unverified
CDS: Cross-Domain Self-Supervised Pre-Training	Jan 1, 2021	Domain AdaptationTransfer Learning	—Unverified
CDR-Adapter: Learning Adapters to Dig Out More Transferring Ability for Cross-Domain Recommendation Models	Nov 4, 2023	Recommendation SystemsTransfer Learning	—Unverified
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning	Apr 4, 2022	Federated LearningKnowledge Distillation	—Unverified
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training	Aug 25, 2021	AttributeSynthetic Data Generation	—Unverified
Adversarial Feature Training for Generalizable Robotic Visuomotor Control	Sep 17, 2019	Deep Reinforcement LearningReinforcement Learning	—Unverified
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis	Jan 1, 2021	Transfer Learning	—Unverified
CCS-GAN: COVID-19 CT-scan classification with very few positive training images	Oct 1, 2021	DiagnosticGenerative Adversarial Network	—Unverified
An X3D Neural Network Analysis for Runner's Performance Assessment in a Wild Sporting Environment	Jul 22, 2023	Action Quality AssessmentAction Recognition	—Unverified
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images	May 16, 2023	Medical DiagnosisTransfer 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