scholarly journals Unsupervised Generation and Synthesis of Facial Images via an Auto-Encoder-Based Deep Generative Adversarial Network

2020 ◽  
Vol 10 (6) ◽  
pp. 1995 ◽  
Author(s):  
Jeong gi Kwak ◽  
Hanseok Ko
Keyword(s):  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Training Scheme ◽  
Adversarial Networks ◽  
High Level ◽  
Facial Images ◽  
And Training ◽  
Double Constraint

The processing of facial images is an important task, because it is required for a large number of real-world applications. As deep-learning models evolve, they require a huge number of images for training. In reality, however, the number of images available is limited. Generative adversarial networks (GANs) have thus been utilized for database augmentation, but they suffer from unstable training, low visual quality, and a lack of diversity. In this paper, we propose an auto-encoder-based GAN with an enhanced network structure and training scheme for Database (DB) augmentation and image synthesis. Our generator and decoder are divided into two separate modules that each take input vectors for low-level and high-level features; these input vectors affect all layers within the generator and decoder. The effectiveness of the proposed method is demonstrated by comparing it with baseline methods. In addition, we introduce a new scheme that can combine two existing images without the need for extra networks based on the auto-encoder structure of the discriminator in our model. We add a novel double-constraint loss to make the encoded latent vectors equal to the input vectors.

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.

scholarly journals Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Linyan Li ◽  
Yu Sun ◽  
Fuyuan Hu ◽  
Tao Zhou ◽  
Xuefeng Xi ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Synthesis ◽  
Semantic Space ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Cascade Structure ◽  
High Resolution Images

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image.

scholarly journals Single Image De-Raining Using Spinning Detail Perceptual Generative Adversarial Networks

2020 ◽  
Vol 24 (7) ◽  
pp. 811-819
Author(s):  
Kaizheng Chen ◽  
◽  
Yaping Dai ◽  
Zhiyang Jia ◽  
Kaoru Hirota
Keyword(s):  
Generative Adversarial Networks ◽  
Perceptual Quality ◽  
Training Procedure ◽  
Single Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Real World Datasets ◽  
High Level

In this paper, Spinning Detail Perceptual Generative Adversarial Networks (SDP-GAN) is proposed for single image de-raining. The proposed method adopts the Generative Adversarial Network (GAN) framework and consists of two following networks: the rain streaks generative network G and the discriminative network D. To reduce the background interference, we propose a rain streaks generative network which not only focuses on the high frequency detail map of rainy image, but also directly reduces the mapping range from input to output. To further improve the perceptual quality of generated images, we modify the perceptual loss by extracting high-level features from discriminative network D, rather than pre-trained networks. Furthermore, we introduce a new training procedure based on the notion of self spinning to improve the final de-raining performance. Extensive experiments on the synthetic and real-world datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods.

scholarly journals BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 688
Author(s):  
Sung-Wook Park ◽  
Jun-Ho Huh ◽  
Jong-Chan Kim
Keyword(s):  
Image Synthesis ◽  
Generative Model ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Compression Performance ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Latent Space ◽  
Boundary Equilibrium ◽  
Space Compression

In the field of deep learning, the generative model did not attract much attention until GANs (generative adversarial networks) appeared. In 2014, Google’s Ian Goodfellow proposed a generative model called GANs. GANs use different structures and objective functions from the existing generative model. For example, GANs use two neural networks: a generator that creates a realistic image, and a discriminator that distinguishes whether the input is real or synthetic. If there are no problems in the training process, GANs can generate images that are difficult even for experts to distinguish in terms of authenticity. Currently, GANs are the most researched subject in the field of computer vision, which deals with the technology of image style translation, synthesis, and generation, and various models have been unveiled. The issues raised are also improving one by one. In image synthesis, BEGAN (Boundary Equilibrium Generative Adversarial Network), which outperforms the previously announced GANs, learns the latent space of the image, while balancing the generator and discriminator. Nonetheless, BEGAN also has a mode collapse wherein the generator generates only a few images or a single one. Although BEGAN-CS (Boundary Equilibrium Generative Adversarial Network with Constrained Space), which was improved in terms of loss function, was introduced, it did not solve the mode collapse. The discriminator structure of BEGAN-CS is AE (AutoEncoder), which cannot create a particularly useful or structured latent space. Compression performance is not good either. In this paper, this characteristic of AE is considered to be related to the occurrence of mode collapse. Thus, we used VAE (Variational AutoEncoder), which added statistical techniques to AE. As a result of the experiment, the proposed model did not cause mode collapse but converged to a better state than BEGAN-CS.

scholarly journals Pal-GAN: Palette-conditioned Generative Adversarial Networks

2021 ◽  
Vol 6 (1) ◽  
pp. 1-5
Author(s):  
Adam Balint ◽  
Graham Taylor
Keyword(s):  
Domain Knowledge ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
High Quality Image ◽  
Common Technique ◽  
Class Labels ◽  
Low Dimensional

Recent advances in Generative Adversarial Networks (GANs) have shown great progress on a large variety of tasks. A common technique used to yield greater diversity of samples is conditioning on class labels. Conditioning on high-dimensional structured or unstructured information has also been shown to improve generation results, e.g. Image-to-Image translation. The conditioning information is provided in the form of human annotations, which can be expensive and difficult to obtain in cases where domain knowledge experts are needed. In this paper, we present an alternative: conditioning on low-dimensional structured information that can be automatically extracted from the input without the need for human annotators. Specifically, we propose a Palette-conditioned Generative Adversarial Network (Pal-GAN), an architecture-agnostic model that conditions on both a colour palette and a segmentation mask for high quality image synthesis. We show improvements on conditional consistency, intersection-over-union, and Fréchet inception distance scores. Additionally, we show that sampling colour palettes significantly changes the style of the generated images.

scholarly journals Realistic Handwriting Generation using Generative Adversarial Networks (Rnn)

2020 ◽  
Vol 9 (1) ◽  
pp. 2049-2052
Keyword(s):  
Short Term Memory ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Cursive Handwriting ◽  
Long Short Term Memory ◽  
High Level ◽  
Online Handwriting ◽  
Handwriting Generation

Generating handwritings of different kinds is quite a challenging task, an area in which not much work has been done yet. Though there has been substantial research done in the area of text recognition, the opposite of handwriting generation. Handwriting generation can prove to be extremely useful for children from blind schools where their speech can get converted into text and be used to generate handwritings of different kinds for them. Handwriting generation also has an important role in field of captcha generation. Our study exhibits in what way recurrent neural networks (RNN) of the type Long Short Term Memory (LSTM) could be used in order to create a composite sequence with structure covering a long range. We propose to use that the Generative Adversarial Network algorithm can be used to generate more realistic handwriting styles with better accuracy than other algorithms. Here, we will be trying to predict one point of data at a time. Our approach is shownfor text, where the type of data is discrete. It can also be used for online handwriting, that is real-valued data. It will then be further drawn out to handwriting generation. The created network will be conditioning its predictions based on a sequence of text. We will be using the resulting system to generate highly realistic cursive handwriting in a wide variety of styles. Experiments that have been carried out on online handwriting databases that are public predict that the method that has been proposed can be used to achieve satisfactory performance, the resultant writing samples achieved a high level of similarity with original samples of handwriting.

PEMBUATAN GAMBAR SINTESIS DARI DEKSRIPSI TEKS DENGAN ALGORITMA GENERATIVE ADVERSARIAL NETWORK

2020 ◽  
Vol 2 (2) ◽  
pp. 111-114
Author(s):  
R Wisnu Prio Pamungkas ◽  
Rakhmi Khalida ◽  
Siti Setiawati
Keyword(s):  
Machine Learning ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Human Intelligence ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

ABSTRACT   Recently computers have been able to produce realistic photos from text. This is one of the potentials of machine learning to be used creatively. Machine learning is the field of solving problems that require an equivalent understanding of human intelligence. In this study using the Generative Adversarial Networks (GAN) algorithm is used to create images from text descriptions. The basic GAN architecture consists of 2 networks called a Generator and Discriminator network. The results of this study is images that are still not detailed in interpreting a text description, but the authors try to produce images that inspire, images can be more poetic when tried using poetry, lyrics, or book quotes. Keywords: GAN, Image Synthesis, Text Description   ABSTRAK   Baru-baru ini komputer mampu menghasilkan foto-foto yang realistis dari sebuah teks. Hal ini adalah salah satu potensi dari machine learning untuk digunakan secara kreatif. Machine learning adalah bidang menyelesaikan masalah-masalah yang membutuhkan pemahaman yang setara dengan kecerdasan manusia. Pada penelitian ini menggunakan algoritme Generative Adversarial Networks (GAN) digunakan untuk menciptakan gambar dari deskripsi teks. Dasar arsitektur GAN terdiri dari 2 jaringan yang disebut sebagai jaringan Generator dan Discriminator. Hasil dari penelitian ini berupa gambar yang masih tidak detail dalam memaknai sebuah deskripsi teks, tetapi penulis mencoba menghasilkan gambar yang menginspirasi, gambar dapat lebih puitis ketika dicoba menggunakan puisi, lirik, atau kutipan buku. Kata Kunci: GAN, Sintesis Gambar, Deskripsi Teks

scholarly journals Generative Adversarial Networks

2021 ◽  
Vol 9 (8) ◽  
pp. 2307-2325
Author(s):  
Amey Thakur
Keyword(s):  
Deep Learning ◽  
Unsupervised Learning ◽  
Video Processing ◽  
Super Resolution ◽  
Image Synthesis ◽  
Background Information ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results. The GAN allows the learning of deep representations in the absence of substantial labelled training information. Computer vision, language and video processing, and image synthesis are just a few of the applications that might benefit from these representations. The purpose of this research is to get the reader conversant with the GAN framework as well as to provide the background information on Generative Adversarial Networks, including the structure of both the generator and discriminator, as well as the various GAN variants along with their respective architectures. Applications of GANs are also discussed with examples. Keywords: Generative Adversarial Networks (GANs), Generator, Discriminator, Supervised and Unsupervised Learning, Discriminative and Generative Modelling, Backpropagation, Loss Functions, Machine Learning, Deep Learning, Neural Networks, Convolutional Neural Network (CNN), Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Information Maximizing GAN (InfoGAN), Stacked GAN (StackGAN), Pix2Pix, Wasserstein GAN (WGAN), Progressive Growing GAN (ProGAN), BigGAN, StyleGAN, CycleGAN, Super-Resolution GAN (SRGAN), Image Synthesis, Image-to-Image Translation.

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.

scholarly journals Restoring Raindrops Using Attentive Generative Adversarial Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 7034
Author(s):  
Hee-Deok Yang
Keyword(s):  
Weather Conditions ◽  
Recurrent Network ◽  
Generative Adversarial Networks ◽  
Navigation Systems ◽  
Vision Systems ◽  
Generative Adversarial Network ◽  
Network Layers ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Outdoor Vision

Artificial intelligence technologies and vision systems are used in various devices, such as automotive navigation systems, object-tracking systems, and intelligent closed-circuit televisions. In particular, outdoor vision systems have been applied across numerous fields of analysis. Despite their widespread use, current systems work well under good weather conditions. They cannot account for inclement conditions, such as rain, fog, mist, and snow. Images captured under inclement conditions degrade the performance of vision systems. Vision systems need to detect, recognize, and remove noise because of rain, snow, and mist to boost the performance of the algorithms employed in image processing. Several studies have targeted the removal of noise resulting from inclement conditions. We focused on eliminating the effects of raindrops on images captured with outdoor vision systems in which the camera was exposed to rain. An attentive generative adversarial network (ATTGAN) was used to remove raindrops from the images. This network was composed of two parts: an attentive-recurrent network and a contextual autoencoder. The ATTGAN generated an attention map to detect rain droplets. A de-rained image was generated by increasing the number of attentive-recurrent network layers. We increased the number of visual attentive-recurrent network layers in order to prevent gradient sparsity so that the entire generation was more stable against the network without preventing the network from converging. The experimental results confirmed that the extended ATTGAN could effectively remove various types of raindrops from images.

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