Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach

2018 ◽  
Vol 19 (3) ◽  
pp. 745-757 ◽  
Author(s):  
Shuai Di ◽  
Honggang Zhang ◽  
Chun-Guang Li ◽  
Xue Mei ◽  
Danil Prokhorov ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Scene Understanding ◽  
Learning Approach ◽  
Cross Domain ◽  
Dense Correspondence

Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach

2018 ◽  
Vol 23 (14) ◽  
pp. 5431-5442 ◽  
Author(s):  
Farhan Hassan Khan ◽  
Usman Qamar ◽  
Saba Bashir
Keyword(s):  
Transfer Learning ◽  
Sentiment Classification ◽  
Learning Approach ◽  
Cross Domain

A Maximum Margin Matrix Factorization based Transfer Learning Approach for Cross-Domain Recommendation

2019 ◽  
Vol 85 ◽  
pp. 105751 ◽  
Author(s):  
Sowmini Devi Veeramachaneni ◽  
Arun K Pujari ◽  
Vineet Padmanabhan ◽  
Vikas Kumar
Keyword(s):  
Transfer Learning ◽  
Matrix Factorization ◽  
Learning Approach ◽  
Maximum Margin ◽  
Cross Domain

A Transfer Learning Approach for Early Diagnosis of Alzheimer’s Disease on MRI Images

Neuroscience ◽  
2021 ◽  
Author(s):  
Atif Mehmood ◽  
Shuyuan yang ◽  
Zhixi feng ◽  
Min wang ◽  
AL Smadi Ahmad ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Diagnosis ◽  
Transfer Learning ◽  
Learning Approach

scholarly journals Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning

2021 ◽  
Vol 11 (9) ◽  
pp. 3952
Author(s):  
Shimin Tang ◽  
Zhiqiang Chen
Keyword(s):  
Deep Learning ◽  
Natural Disaster ◽  
Scene Understanding ◽  
Computing Methods ◽  
Classification Model ◽  
Learning Approach ◽  
Learning Models ◽  
Damage Level ◽  
Feature Extractor ◽  
Mobile Imaging

With the ubiquitous use of mobile imaging devices, the collection of perishable disaster-scene data has become unprecedentedly easy. However, computing methods are unable to understand these images with significant complexity and uncertainties. In this paper, the authors investigate the problem of disaster-scene understanding through a deep-learning approach. Two attributes of images are concerned, including hazard types and damage levels. Three deep-learning models are trained, and their performance is assessed. Specifically, the best model for hazard-type prediction has an overall accuracy (OA) of 90.1%, and the best damage-level classification model has an explainable OA of 62.6%, upon which both models adopt the Faster R-CNN architecture with a ResNet50 network as a feature extractor. It is concluded that hazard types are more identifiable than damage levels in disaster-scene images. Insights are revealed, including that damage-level recognition suffers more from inter- and intra-class variations, and the treatment of hazard-agnostic damage leveling further contributes to the underlying uncertainties.

A novel transfer learning approach for the classification of histological images of colorectal cancer

2021 ◽  
Author(s):  
Elene Firmeza Ohata ◽  
João Victor Souza das Chagas ◽  
Gabriel Maia Bezerra ◽  
Mohammad Mehedi Hassan ◽  
Victor Hugo Costa de Albuquerque ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Transfer Learning ◽  
Learning Approach ◽  
Histological Images
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