A Distributed Federated Transfer Learning Framework for Edge Optical Network

2020 ◽  
Author(s):  
Hui Yang ◽  
Qiuyan Yao ◽  
Jie Zhang
Keyword(s):  
Transfer Learning ◽  
Optical Network ◽  
Learning Framework

scholarly journals A 3-CS Distributed Federated Transfer Learning Framework for Intelligent Edge Optical Networks

2021 ◽  
Vol 2 ◽  
Author(s):  
Hui Yang ◽  
Qiuyan Yao ◽  
Bowen Bao ◽  
Chao Li ◽  
Danshi Wang ◽  
...  
Keyword(s):  
Optical Networks ◽  
Transfer Learning ◽  
Network Performance ◽  
Rapid Development ◽  
Optical Network ◽  
Operation Efficiency ◽  
Cross Spectrum ◽  
Distributed Intelligence ◽  
Learning Framework ◽  
Intelligent Approach

With the rapid development of optical network and edge computing, the operation efficiency of the edge optical network has become more and more important, requiring an intelligent approach to enhance the network performance. To enhance the intelligence of the edge optical network, this article firstly provides the demand for the development of edge optical networks. Then, a cross-scene, cross-spectrum, and cross-service (3-CS) architecture for edge optical networks is presented. Finally, a federated transfer learning (FTL) framework, realizing a distributed intelligence edge optical network, is proposed. The usability of the proposed framework is verified by simulation.

scholarly journals A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation

2021 ◽  
Author(s):  
Yin Zhang ◽  
Derek Zhiyuan Cheng ◽  
Tiansheng Yao ◽  
Xinyang Yi ◽  
Lichan Hong ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Long Tail ◽  
Learning Framework

scholarly journals Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vishu Gupta ◽  
Kamal Choudhary ◽  
Francesca Tavazza ◽  
Carelyn Campbell ◽  
Wei-keng Liao ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Predictive Models ◽  
Materials Science ◽  
Predictive Analytics ◽  
Large Datasets ◽  
Small Data ◽  
Widespread Application ◽  
Learning Framework ◽  
Large Databases ◽  
Physical Attributes

AbstractArtificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

FT-MDnet: A Deep-Frozen Transfer Learning Framework for Person Search

2021 ◽  
pp. 1-1
Author(s):  
Ronghua Hu ◽  
Tian Wang ◽  
Yi Zhou ◽  
Hichem Snoussi ◽  
Abel Cherouat
Keyword(s):  
Transfer Learning ◽  
Learning Framework ◽  
Person Search ◽  
Frozen Transfer

scholarly journals A Multiple Source based Transfer Learning Framework for Marketing Campaigns

2018 ◽  
Author(s):  
James Brownlow ◽  
Charles Chu ◽  
Guandong Xu ◽  
Ben Culbert ◽  
Bin Fu ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Multiple Source ◽  
Learning Framework

scholarly journals Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jianye Zhou ◽  
Xinyu Yang ◽  
Lin Zhang ◽  
Siyu Shao ◽  
Gangying Bian
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
High Efficiency ◽  
Image Resolution ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Multiple Sensors ◽  
Learning Framework ◽  
Training Samples ◽  
Model Training

To realize high-precision and high-efficiency machine fault diagnosis, a novel deep learning framework that combines transfer learning and transposed convolution is proposed. Compared with existing methods, this method has faster training speed, fewer training samples per time, and higher accuracy. First, the raw data collected by multiple sensors are combined into a graph and normalized to facilitate model training. Next, the transposed convolution is utilized to expand the image resolution, and then the images are treated as the input of the transfer learning model for training and fine-tuning. The proposed method adopts 512 time series to conduct experiments on two main mechanical datasets of bearings and gears in the variable-speed gearbox, which verifies the effectiveness and versatility of the method. We have obtained advanced results on both datasets of the gearbox dataset. The dataset shows that the test accuracy is 99.99%, achieving a significant improvement from 98.07% to 99.99%.

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