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 2971–2980 of 10307 papers

Title	Date	Tasks	Status	Hype
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs	Aug 4, 2023	Knowledge DistillationQuantization	CodeCode Available	1
Deep Maxout Network-based Feature Fusion and Political Tangent Search Optimizer enabled Transfer Learning for Thalassemia Detection	Aug 3, 2023	Data AugmentationTransfer Learning	—Unverified	0
Deep Learning-based Prediction of Stress and Strain Maps in Arterial Walls for Improved Cardiovascular Risk Assessment	Aug 3, 2023	Generative Adversarial NetworkSSIM	—Unverified	0
Curricular Transfer Learning for Sentence Encoded Tasks	Aug 3, 2023	SentenceTransfer Learning	—Unverified	0
IndoHerb: Indonesia Medicinal Plants Recognition using Transfer Learning and Deep Learning	Aug 3, 2023	Transfer Learning	CodeCode Available	0
A vision transformer-based framework for knowledge transfer from multi-modal to mono-modal lymphoma subtyping models	Aug 2, 2023	Knowledge DistillationTransfer Learning	—Unverified	0
Pixel to policy: DQN Encoders for within & cross-game reinforcement learning	Aug 1, 2023	reinforcement-learningTransfer Learning	—Unverified	0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning	Aug 1, 2023	ClassificationDecision Making	—Unverified	0
Understanding Activation Patterns in Artificial Neural Networks by Exploring Stochastic Processes	Aug 1, 2023	MemorizationTransfer Learning	—Unverified	0
Cardiac MRI Orientation Recognition and Standardization using Deep Neural Networks	Jul 31, 2023	Transfer Learning	CodeCode Available	0

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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