An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images

[1]  Erik Cambria,et al.  Recent Trends in Deep Learning Based Natural Language Processing , 2017, IEEE Comput. Intell. Mag..

[2]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[3]  Ekin D. Cubuk,et al.  A Fourier Perspective on Model Robustness in Computer Vision , 2019, NeurIPS.

[4]  Geoffrey Zweig,et al.  An introduction to computational networks and the computational network toolkit (invited talk) , 2014, INTERSPEECH.

[5]  Matthias Bethge,et al.  Increasing the robustness of DNNs against image corruptions by playing the Game of Noise , 2020, ArXiv.

[6]  Tara N. Sainath,et al.  Deep Learning for Audio Signal Processing , 2019, IEEE Journal of Selected Topics in Signal Processing.

[7]  Gregory Shakhnarovich,et al.  Examining the Impact of Blur on Recognition by Convolutional Networks , 2016, ArXiv.

[8]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[9]  Lina J. Karam,et al.  A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions , 2017, 2017 26th International Conference on Computer Communication and Networks (ICCCN).

[10]  Lina J. Karam,et al.  Understanding how image quality affects deep neural networks , 2016, 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX).

[11]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[12]  Carsten Rother,et al.  Benchmarking the Robustness of Semantic Segmentation Models with Respect to Common Corruptions , 2020, Int. J. Comput. Vis..