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 4001–4010 of 10307 papers

Title	Date	Tasks	Status	Hype
Unveiling the Tapestry: the Interplay of Generalization and Forgetting in Continual Learning	Nov 21, 2022	AI AgentContinual Learning	—Unverified	0
Novel transfer learning schemes based on Siamese networks and synthetic data	Nov 21, 2022	Transfer Learning	CodeCode Available	0
Towards continually learning new languages	Nov 21, 2022	Allspeech-recognition	—Unverified	0
Federated deep transfer learning for EEG decoding using multiple BCI tasks	Nov 20, 2022	DecoderEEG	—Unverified	0
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization	Nov 20, 2022	Android Malware DetectionFederated Learning	—Unverified	0
Frozen Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks	Nov 20, 2022	image-classificationImage Classification	—Unverified	0
Font Representation Learning via Paired-glyph Matching	Nov 20, 2022	Font Style TransferRepresentation Learning	CodeCode Available	1
A Comparative Analysis of Transfer Learning-based Techniques for the Classification of Melanocytic Nevi	Nov 20, 2022	Transfer Learning	—Unverified	0
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors	Nov 19, 2022	Generative Adversarial NetworkPrivacy Preserving	—Unverified	0
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network	Nov 19, 2022	Generative Adversarial NetworkImage Super-Resolution	—Unverified	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