Results 261 to 270 of about 10,380,352 (315)

A Survey on Transfer Learning

IEEE Transactions on Knowledge and Data Engineering, 2010
A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold.
Sinno Jialin Pan, Qiang Yang
semanticscholar   +1 more source

Transfer Learning

Transfer ...
Pratik Mungekar, Nerurkar, Pranav Ajeet
  +5 more sources

A Survey on Deep Transfer Learning

International Conference on Artificial Neural Networks, 2018
As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains.
Chuanqi Tan, F. Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu   +5 more
semanticscholar   +1 more source

Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings

IEEE transactions on industrial electronics (1982. Print), 2022
Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes.
Zhijian Wang, Xinxin He, B. Yang, Naipeng Li   +3 more
semanticscholar   +1 more source

Rethinking Membership Inference Attacks Against Transfer Learning

IEEE Transactions on Information Forensics and Security
Transfer learning, successful in knowledge translation across related tasks, faces a substantial privacy threat from membership inference attacks (MIAs).
Cong Wu, Jing Chen, Qianru Fang, Kun He, Ziming Zhao, Hao Ren, Guowen Xu, Yang Liu, Yang Xiang   +8 more
semanticscholar   +1 more source

Transfer Learning and Ensemble Learning

2020
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.
openaire   +1 more source

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

IEEE Transactions on Industrial Informatics, 2019
We develop a novel deep learning framework to achieve highly accurate machine fault diagnosis using transfer learning to enable and accelerate the training of deep neural network. Compared with existing methods, the proposed method is faster to train and
Siyu Shao, S. McAleer, Ruqiang Yan, P. Baldi   +3 more
semanticscholar   +1 more source

Learning Transfer

Proceedings of the Third (2016) ACM Conference on Learning @ Scale, 2016
The rising number of Massive Open Online Courses (MOOCs) enable people to advance their knowledge and competencies in a wide range of fields. Learning though is only the first step, the transfer of the taught concepts into practice is equally important and often neglected in the investigation of MOOCs.
Guanliang Chen, Dan Davis, Claudia Hauff, Geert-Jan Houben   +3 more
openaire   +1 more source

Transfer Learning

2010
Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community.
Lisa Torrey, Jude Shavlik
openaire   +1 more source

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Journal of Biomolecular Structure and Dynamics, 2020
Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets.
Aayush Jaiswal, Neha Gianchandani, Dilbag Singh, Vijay Kumar, Manjit Kaur   +4 more
semanticscholar   +1 more source