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 3601–3610 of 10307 papers

Title	Date	Tasks	Status
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning	Dec 21, 2023	Domain GeneralizationFederated Learning	CodeCode Available
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0	Dec 21, 2023	Speech SynthesisTransfer Learning	—Unverified
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning	Dec 21, 2023	Contrastive LearningGraph Representation Learning	CodeCode Available
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate Datasets	Dec 20, 2023	Transfer Learning	CodeCode Available
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification	Dec 20, 2023	Anomaly DetectionClassification	—Unverified
Bayesian Transfer Learning	Dec 20, 2023	Electrical EngineeringTransfer Learning	—Unverified
Deep transfer learning for visual analysis and attribution of paintings by Raphael	Dec 20, 2023	Edge DetectionTransfer Learning	CodeCode Available
Value Explicit Pretraining for Learning Transferable Representations	Dec 19, 2023	Transfer LearningTransfer Reinforcement Learning	—Unverified
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery	Dec 19, 2023	Representation LearningTransfer Learning	CodeCode Available
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer	Dec 19, 2023	Transfer 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