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 2541–2550 of 10307 papers

Title	Date	Tasks	Status	Score
Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning	Aug 2, 2021	Meta-LearningTransfer Learning	CodeCode Available	5
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations	Jun 6, 2022	Contour DetectionPose Estimation	CodeCode Available	5
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AI	Sep 10, 2024	Meta-LearningTransfer Learning	CodeCode Available	5
Few-shot classification in Named Entity Recognition Task	Dec 14, 2018	ClassificationGeneral Classification	CodeCode Available	5
Feudal Graph Reinforcement Learning	Apr 11, 2023	Decision MakingGraph Clustering	CodeCode Available	5
A Tulu Resource for Machine Translation	Mar 28, 2024	Machine TranslationNMT	CodeCode Available	5
Few-Shot Fruit Segmentation via Transfer Learning	May 4, 2024	Few-Shot Semantic SegmentationScene Parsing	CodeCode Available	5
Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations	Nov 7, 2022	ClusteringRecommendation Systems	CodeCode Available	5
Few-Shot Learning for Image Classification of Common Flora	May 7, 2021	ClassificationFew-Shot Image Classification	CodeCode Available	5
FedPCL-CDR: A Federated Prototype-based Contrastive Learning Framework for Privacy-Preserving Cross-domain Recommendation	Sep 5, 2024	Contrastive LearningFederated Learning	CodeCode Available	5

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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