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 3381–3390 of 10307 papers

Title	Date	Tasks	Status
Towards Unbiased Training in Federated Open-world Semi-supervised Learning	May 1, 2023	Open-World Semi-Supervised LearningTransfer Learning	—Unverified
Redundancy and Concept Analysis for Code-trained Language Models	May 1, 2023	MemorizationModel Compression	—Unverified
Procedural Content Generation via Knowledge Transformation (PCG-KT)	May 1, 2023	Transfer Learning	—Unverified
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity	May 1, 2023	FairnessStudent dropout	CodeCode Available
Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization	Apr 30, 2023	feature selectionTransfer Learning	—Unverified
Few-shot Classification via Ensemble Learning with Multi-Order Statistics	Apr 30, 2023	ClassificationDiversity	—Unverified
The ART of Transfer Learning: An Adaptive and Robust Pipeline	Apr 30, 2023	Learning TheorySparse Learning	—Unverified
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings	Apr 30, 2023	reinforcement-learningReinforcement Learning	—Unverified
Transfer of knowledge among instruments in automatic music transcription	Apr 30, 2023	Information RetrievalMusic Information Retrieval	—Unverified
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge	Apr 29, 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