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 1641–1650 of 10307 papers

Title	Date	Tasks	Status
A Food Photography App with Image Recognition for Thai Food	Jul 1, 2018	Image RecognitionTransfer Learning	—Unverified
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications	Jan 20, 2020	Reinforcement LearningTransfer Learning	—Unverified
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks	Oct 6, 2015	Active LearningGeneral Classification	—Unverified
A Review on Discriminative Self-supervised Learning Methods in Computer Vision	May 8, 2024	ClusteringKnowledge Distillation	—Unverified
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery	Aug 18, 2023	Date UnderstandingDomain Adaptation	—Unverified
A Foliated View of Transfer Learning	Aug 2, 2020	Transfer Learning	—Unverified
Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy	Nov 27, 2023	Deep Reinforcement LearningReinforcement Learning (RL)	—Unverified
A review of sentiment analysis research in Arabic language	May 25, 2020	Arabic Sentiment AnalysisMachine Translation	—Unverified
A Review of Deep Transfer Learning and Recent Advancements	Jan 19, 2022	Deep LearningTransfer Learning	—Unverified
A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection	Oct 25, 2024	Binary ClassificationDeep 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