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 3521–3530 of 10307 papers

Title	Date	Tasks	Status	Hype
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing	Mar 26, 2023	Transfer Learning	—Unverified	0
Δ-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss	Mar 26, 2023	Domain AdaptationTransfer Learning	—Unverified	0
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning	Mar 26, 2023	Transfer LearningVisual Prompting	CodeCode Available	1
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation	Mar 25, 2023	Continual LearningKnowledge Distillation	—Unverified	0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection	Mar 25, 2023	Domain AdaptationIntrusion Detection	—Unverified	0
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks	Mar 25, 2023	Federated LearningGraph Learning	—Unverified	0
Federated Learning without Full Labels: A Survey	Mar 25, 2023	Federated LearningSelf-Supervised Learning	—Unverified	0
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning	Mar 24, 2023	Transfer Learning	—Unverified	0
Unknown Sniffer for Object Detection: Don't Turn a Blind Eye to Unknown Objects	Mar 24, 2023	Objectobject-detection	CodeCode Available	1
Decoupled Multimodal Distilling for Emotion Recognition	Mar 24, 2023	Emotion RecognitionKnowledge Distillation	CodeCode Available	1

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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