Towards landmarks prediction with Deep Network

2018 ◽  
Author(s):  
Van Linh Le ◽  
M. Beurton-Aimar ◽  
A. Zemmari ◽  
N. Parisey
Keyword(s):  
Deep Network

A robust and fast multispectral pedestrian detection deep network

2021 ◽  
pp. 106990
Author(s):  
Lu Ding ◽  
Yong Wang ◽  
Robert Laganière ◽  
Dan Huang ◽  
Xinbin Luo ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Deep Network

scholarly journals ChangeGAN: A Deep Network for Change Detection in Coarsely Registered Point Clouds

2021 ◽  
Vol 6 (4) ◽  
pp. 8277-8284
Author(s):  
Balazs Nagy ◽  
Lorant Kovacs ◽  
Csaba Benedek
Keyword(s):  
Change Detection ◽  
Point Clouds ◽  
Deep Network

scholarly journals An aggregate method for thorax diseases classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bayu Adhi Nugroho
Keyword(s):  
Network Architecture ◽  
Classification Problem ◽  
Calculation Algorithm ◽  
Training Pattern ◽  
Deep Network ◽  
Network Training ◽  
Chest X Ray ◽  
Medical Image Classification ◽  
Positive Pattern

AbstractA common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.

scholarly journals Single-image SVBRDF capture with a rendering-aware deep network

2018 ◽  
Vol 37 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Valentin Deschaintre ◽  
Miika Aittala ◽  
Fredo Durand ◽  
George Drettakis ◽  
Adrien Bousseau
Keyword(s):  
Single Image ◽  
Deep Network
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