scholarly journals Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields

2017 ◽  
Author(s):  
Alessandro Corbetta ◽  
Vlado Menkovski ◽  
Federico Toschi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Supervised Training ◽  
Weakly Supervised

scholarly journals WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks With Multiscale Evidence

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20449-20458
Author(s):  
Yongsheng Liu ◽  
Wenyu Chen ◽  
Hong Qu ◽  
Tianlei Wang ◽  
Jiangzhou Ji ◽  
...  
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 120078-120087
Author(s):  
Kexin Li ◽  
Jingzhe Zhang ◽  
Yunfei Sun ◽  
Xinwang Huang ◽  
Chunxue Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Bone Age ◽  
Deep Convolutional Neural Networks ◽  
Age Assessment ◽  
Bone Age Assessment ◽  
Weakly Supervised

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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