DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network

Linear predictive coding is an extremely effective voice generation method that operates through simple process. However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. This model generates voices with similar semantics through the random input from the latent space of generator. The experimental results indicate that voices generated exclusively by generative adversarial networks exhibit excessive noise. To solve this problem, a denoise autoencoder was connected to the generator for denoising. The experimental results prove the feasibility of the proposed voice generation method. In the future, this method can be applied in robots and voice generation applications to increase the humanistic language expression ability of robots and enable robots to demonstrate more humanistic and natural speaking performance.

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Event Factuality Identification via Generative Adversarial Networks with Auxiliary Classification

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/597 ◽

2018 ◽

Cited By ~ 2

Author(s):

Zhong Qian ◽

Peifeng Li ◽

Yue Zhang ◽

Guodong Zhou ◽

Qiaoming Zhu

Keyword(s):

State Of The Art ◽

Experimental Results ◽

Generative Adversarial Networks ◽

Semantic Task ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Traditional Research ◽

Syntactic Information ◽

Event Factuality

Event factuality identification is an important semantic task in NLP. Traditional research heavily relies on annotated texts. This paper proposes a two-step framework, first extracting essential factors related with event factuality from raw texts as the input, and then identifying the factuality of events via a Generative Adversarial Network with Auxiliary Classification (AC-GAN). The use of AC-GAN allows the model to learn more syntactic information and address the imbalance among factuality values. Experimental results on FactBank show that our method significantly outperforms several state-of-the-art baselines, particularly on events with embedded sources, speculative and negative factuality values.

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BEGAN v3: Avoiding Mode Collapse in GANs Using Variational Inference

Electronics ◽

10.3390/electronics9040688 ◽

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.

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Synthesizing Designs With Inter-Part Dependencies Using Hierarchical Generative Adversarial Networks

Volume 2A: 44th Design Automation Conference ◽

10.1115/detc2018-85339 ◽

2018 ◽

Cited By ~ 3

Author(s):

Wei Chen ◽

Ashwin Jeyaseelan ◽

Mark Fuge

Keyword(s):

Real World ◽

Generative Models ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Design Exploration ◽

Adversarial Network ◽

Adversarial Networks ◽

Latent Space ◽

Latent Representations

Real-world designs usually consist of parts with hierarchical dependencies, i.e., the geometry of one component (a child shape) is dependent on another (a parent shape). We propose a method for synthesizing this type of design. It decomposes the problem of synthesizing the whole design into synthesizing each component separately but keeping the inter-component dependencies satisfied. This method constructs a two-level generative adversarial network to train two generative models for parent and child shapes, respectively. We then use the trained generative models to synthesize or explore parent and child shapes separately via a parent latent representation and infinite child latent representations, each conditioned on a parent shape. We evaluate and discuss the disentanglement and consistency of latent representations obtained by this method. We show that shapes change consistently along any direction in the latent space. This property is desirable for design exploration over the latent space.

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ORGANIC (1).pdf

10.26434/chemrxiv.5309668.v1 ◽

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.

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Restoring Raindrops Using Attentive Generative Adversarial Networks

Applied Sciences ◽

10.3390/app11157034 ◽

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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Enhanced network optimized generative adversarial network for image enhancement

Multimedia Tools and Applications ◽

10.1007/s11042-020-10310-z ◽

2021 ◽

Author(s):

Lingyu Yan ◽

Jiarun Fu ◽

Chunzhi Wang ◽

Zhiwei Ye ◽

Hongwei Chen ◽

...

Keyword(s):

Image Enhancement ◽

Image Recognition ◽

Generative Adversarial Networks ◽

Low Light ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Enhancement Method ◽

New Space ◽

Traditional Image

AbstractWith the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

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Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network

European Radiology ◽

10.1007/s00330-021-07714-2 ◽

2021 ◽

Author(s):

Johannes Haubold ◽

René Hosch ◽

Lale Umutlu ◽

Axel Wetter ◽

Patrizia Haubold ◽

...

Keyword(s):

Image Quality ◽

Contrast Media ◽

Animal Experiments ◽

Turing Test ◽

Dual Energy ◽

Generative Adversarial Networks ◽

Arterial Phase ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks

Abstract Objectives To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. Methods Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. Results The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. Conclusions The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. Key Points • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. • Not only the image quality but especially the pathological consistency must be evaluated to assess safety. • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%.

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Linear electromagnetic inverse scattering via generative adversarial networks

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078721001331 ◽

2021 ◽

pp. 1-9

Author(s):

Huilin Zhou ◽

Huimin Zheng ◽

Qiegen Liu ◽

Jian Liu ◽

Yuhao Wang

Keyword(s):

Inverse Scattering ◽

Optimization Methods ◽

Training Data ◽

Generative Adversarial Networks ◽

Scattering Problems ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Highly Nonlinear ◽

Electromagnetic Inverse Scattering

Abstract Electromagnetic inverse-scattering problems (ISPs) are concerned with determining the properties of an unknown object using measured scattered fields. ISPs are often highly nonlinear, causing the problem to be very difficult to address. In addition, the reconstruction images of different optimization methods are distorted which leads to inaccurate reconstruction results. To alleviate these issues, we propose a new linear model solution of generative adversarial network-based (LM-GAN) inspired by generative adversarial networks (GAN). Two sub-networks are trained alternately in the adversarial framework. A linear deep iterative network as a generative network captures the spatial distribution of the data, and a discriminative network estimates the probability of a sample from the training data. Numerical results validate that LM-GAN has admirable fidelity and accuracy when reconstructing complex scatterers.

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Towards Accuracy Enhancement of Age Group Classification Using Generative Adversarial Networks

Journal of Integrated Design and Process Science ◽

10.3233/jid-210019 ◽

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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Best Selection of Generative Adversarial Networks Hyper-Parameters using Genetic Algorithm

10.21203/rs.3.rs-95571/v1 ◽

2020 ◽

Author(s):

Fajr Alarsan ◽

Mamoon Younes

Keyword(s):

Genetic Algorithm ◽

Programming Language ◽

Loss Function ◽

Generative Adversarial Networks ◽

Accuracy Function ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Parameters Selection ◽

Selection Of

Abstract Generative Adversarial Networks (GANs) are most popular generative frameworks that have achieved compelling performance. They follow an adversarial approach where two deep models generator and discriminator compete with each other In this paper, we propose a Generative Adversarial Network with best hyper-parameters selection to generate fake images for digits number 1 to 9 with generator and train discriminator to decide whereas the generated images are fake or true. Using Genetic Algorithm technique to adapt GAN hyper-parameters, the final method is named GANGA:Generative Adversarial Network with Genetic Algorithm. Anaconda environment with tensorflow library facilitates was used, python as programming language also used with needed libraries. The implementation was done using MNIST dataset to validate our work. The proposed method is to let Genetic algorithm to choose best values of hyper-parameters depending on minimizing a cost function such as a loss function or maximizing accuracy function. GA was used to select values of Learning rate, Batch normalization, Number of neurons and a parameter of Dropout layer.

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