Facial Age Progression using Conditional Generative Adversarial Network with Heritable Visual Features

2021 ◽  
Author(s):  
Prarinya Siritanawan ◽  
Hideki Ichikawa ◽  
Kazunori Kotani
Keyword(s):  
Visual Features ◽  
Generative Adversarial Network ◽  
Age Progression ◽  
Adversarial Network ◽  
Facial Age

scholarly journals Semi-Supervised FaceGAN for Face-Age Progression and Regression with Synthesized Paired Images

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 603
Author(s):  
Quang T. M. Pham ◽  
Janghoon Yang ◽  
Jitae Shin
Keyword(s):  
Real Data ◽  
Similarity Score ◽  
Generative Adversarial Network ◽  
Age Progression ◽  
Regression Methods ◽  
Adversarial Network ◽  
Face Images ◽  
The Face ◽  
Quantitative Results ◽  
Human Faces

The performance of existing face age progression or regression methods is often limited by the lack of sufficient data to train the model. To deal with this problem, we introduce a novel framework that exploits synthesized images to improve the performance. A conditional generative adversarial network (GAN) is first developed to generate facial images with targeted ages. The semi-supervised GAN, called SS-FaceGAN, is proposed. This approach considers synthesized images with a target age and the face images from the real data so that age and identity features can be explicitly utilized in the objective function of the network. We analyze the performance of our method over previous studies qualitatively and quantitatively. The experimental results show that the SS-FaceGAN model can produce realistic human faces in terms of both identity preservation and age preservation with the quantitative results of a decent face detection rate of 97% and similarity score of 0.30 on average.

Facial Age Synthesis With Label Distribution-Guided Generative Adversarial Network

2020 ◽  
Vol 15 ◽  
pp. 2679-2691 ◽  
Author(s):  
Yunlian Sun ◽  
Jinhui Tang ◽  
Xiangbo Shu ◽  
Zhenan Sun ◽  
Massimo Tistarelli
Keyword(s):  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Label Distribution ◽  
Facial Age

scholarly journals Contrast invariant tuning in human perception of image content

2019 ◽  
Author(s):  
Ingo Fruend ◽  
Jaykishan Patel ◽  
Elee D. Stalker
Keyword(s):  
Visual Processing ◽  
Discrimination Performance ◽  
Human Perception ◽  
Image Features ◽  
Image Contrast ◽  
Visual Features ◽  
Image Content ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Contrast Discrimination

AbstractHigher levels of visual processing are progressively more invariant to low-level visual factors such as contrast. Although this invariance trend has been well documented for simple stimuli like gratings and lines, it is difficult to characterize such invariances in images with naturalistic complexity. Here, we use a generative image model based on a hierarchy of learned visual features—a Generative Adversarial Network—to constrain image manipulations to remain within the vicinity of the manifold of natural images. This allows us to quantitatively characterize visual discrimination behaviour for naturalistically complex, non-linear image manipulations. We find that human tuning to such manipulations has a factorial structure. The first factor governs image contrast with discrimination thresholds following a power law with an exponent between 0.5 and 0.6, similar to contrast discrimination performance for simpler stimuli. A second factor governs image content with approximately constant discrimination thresholds throughout the range of images studied. These results support the idea that human perception factors out image contrast relatively early on, allowing later stages of processing to extract higher level image features in a stable and robust way.

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network
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