Generative adversarial networks- and ResNets-based framework for image translation with super-resolution

2018 ◽  
Vol 27 (06) ◽  
pp. 1 ◽  
Author(s):  
Hui Hu
Keyword(s):  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Translation

scholarly journals Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1705
Author(s):  
Aziz Alotaibi
Keyword(s):  
Three Dimensional ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Comprehensive Overview ◽  
Adversarial Networks ◽  
Current State ◽  
Image Translation ◽  
Translation Techniques ◽  
Future Research Directions

Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed.

Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

2021 ◽  
pp. 1-17
Author(s):  
Khaled ELKarazle ◽  
Valliappan Raman ◽  
Patrick Then
Keyword(s):  
Age Estimation ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Age Group Classification ◽  
Facial Images

Age estimation models can be employed in many applications, including soft biometrics, content access control, targeted advertising, and many more. However, as some facial images are taken in unrestrained conditions, the quality relegates, which results in the loss of several essential ageing features. This study investigates how introducing a new layer of data processing based on a super-resolution generative adversarial network (SRGAN) model can influence the accuracy of age estimation by enhancing the quality of both the training and testing samples. Additionally, we introduce a novel convolutional neural network (CNN) classifier to distinguish between several age classes. We train one of our classifiers on a reconstructed version of the original dataset and compare its performance with an identical classifier trained on the original version of the same dataset. Our findings reveal that the classifier which trains on the reconstructed dataset produces better classification accuracy, opening the door for more research into building data-centric machine learning systems.

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