Deep Convolutional Generative Adversarial Networks Research Articles

Enforcing Traffic Safety: A Deep Learning Approach for Detecting Motorcyclists’ Helmet Violations Using YOLOv8 and Deep Convolutional Generative Adversarial Network-Generated Images

In this study, we introduce an innovative methodology for the detection of helmet usage violations among motorcyclists, integrating the YOLOv8 object detection algorithm with deep convolutional generative adversarial networks (DCGANs). The objective of this research is to enhance the precision of existing helmet violation detection techniques, which are typically reliant on manual inspection and susceptible to inaccuracies. The proposed methodology involves model training on an extensive dataset comprising both authentic and synthetic images, and demonstrates high accuracy in identifying helmet violations, including scenarios with multiple riders. Data augmentation, in conjunction with synthetic images produced by DCGANs, is utilized to expand the training data volume, particularly focusing on imbalanced classes, thereby facilitating superior model generalization to real-world circumstances. The stand-alone YOLOv8 model exhibited an F1 score of 0.91 for all classes at a confidence level of 0.617, whereas the DCGANs + YOLOv8 model demonstrated an F1 score of 0.96 for all classes at a reduced confidence level of 0.334. These findings highlight the potential of DCGANs in enhancing the accuracy of helmet rule violation detection, thus fostering safer motorcycling practices.

An Adaptive Enhancement Method of Malicious Traffic Samples Based on DCGAN-ResNet System

A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model, to enhance the quality of generated samples. The device can switch states to adapt to various network environments and has been experimentally validated for its effectiveness and feasibility.Moreover, employing an adaptive device, the samples of malicious traffic are adjusted. Experimental analysis demonstrates that the device significantly enhances the accuracy of anomaly traffic detection, improves robustness, and provides robust support for network security protection.

DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks

BackgroundIn recent years, there has been a growing interest in utilizing computational approaches to predict drug-target binding affinity, aiming to expedite the early drug discovery process. To address the limitations of experimental methods, such as cost and time, several machine learning-based techniques have been developed. However, these methods encounter certain challenges, including the limited availability of training data, reliance on human intervention for feature selection and engineering, and a lack of validation approaches for robust evaluation in real-life applications.ResultsTo mitigate these limitations, in this study, we propose a method for drug-target binding affinity prediction based on deep convolutional generative adversarial networks. Additionally, we conducted a series of validation experiments and implemented adversarial control experiments using straw models. These experiments serve to demonstrate the robustness and efficacy of our predictive models. We conducted a comprehensive evaluation of our method by comparing it to baselines and state-of-the-art methods. Two recently updated datasets, namely the BindingDB and PDBBind, were used for this purpose. Our findings indicate that our method outperforms the alternative methods in terms of three performance measures when using warm-start data splitting settings. Moreover, when considering physiochemical-based cold-start data splitting settings, our method demonstrates superior predictive performance, particularly in terms of the concordance index.ConclusionThe results of our study affirm the practical value of our method and its superiority over alternative approaches in predicting drug-target binding affinity across multiple validation sets. This highlights the potential of our approach in accelerating drug repurposing efforts, facilitating novel drug discovery, and ultimately enhancing disease treatment. The data and source code for this study were deposited in the GitHub repository, https://github.com/mojtabaze7/DCGAN-DTA. Furthermore, the web server for our method is accessible at https://dcgan.shinyapps.io/bindingaffinity/.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes invivo. We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO-CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology.

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning.

Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accuracy of prognosis may also be affected by way of doctor inexperience and fatigue, underscoring the want for automatic classification fashions that can analyze UF from a giant wide variety of images. A hybrid model has been proposed that combines the MobileNetV2 community and deep convolutional generative adversarial networks (DCGAN) into useful resources for medical practitioners in figuring out UF and evaluating its characteristics. Real-time automated classification of UF can aid in diagnosing the circumstance and minimizing subjective errors. The DCGAN science is utilized for superior statistics augmentation to create first-rate UF images, which are labeled into UF and non-uterine-fibroid (NUF) classes. The MobileNetV2 model then precisely classifies the photos based totally on this data. The overall performance of the hybrid model contrasts with different models. The hybrid model achieves a real-time classification velocity of 40 frames per second (FPS), an accuracy of 97.45%, and an F1 rating of 0.9741. By using this deep learning hybrid approach, we address the shortcomings of the current classification methods of uterine fibroid.

Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

Application of deep convolutional generative adversarial network to identification of bridge structural damage

Application of deep convolutional generative adversarial network to identification of bridge structural damage

Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN–LSTM model

Background and Objective:People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. Methods:This study quantified the functional connectivity of the subject’s brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. Results:Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. Conclusions:This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.

Intelligent Security Authentication for Connected and Autonomous Vehicles: Attacks and Defenses

The emergence of integrated positioning, communication, and sensing technologies has paved the way for a surge in connected and autonomous vehicles. The control system has been successful in reliable and fast transmission. However, practical applications face security risks, especially data tampering and spoofing attacks. To improve the resilience of the system against potential attacks, we attempt to leverage a generative adversarial network learning-assisted authentication framework (GAF). In addition to proposing a new method for validating vehicles, we also introduce a new architectural innovation in the generator–discriminator pair to achieve improved results. The generator sub-network is constructed using an advanced convolutional neural network, whereas the discriminator is designed to leverage global and local information to determine whether a signal is real or fake. On this basis, we propose a signal enhancement-based authentication method, a deep convolutional generative adversarial network (DCGAN). Experimental results using the National Institute of Standards and Technology (NIST) dataset show that the proposed method is effective in denoising and improving the detection performance.

Using machine learning for chemical-free histological tissue staining

ABSTRACT Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to ‘virtually’ stain unstained tissue images indistinguishable from chemically stained tissue. Frameworks such as deep convolutional GANs (DCGAN) and conditional GANs (CGANs) have successfully generated highly reproducible ‘stained’ images. However, their utility may be limited by requiring registered, paired images which can be difficult to obtain. To avoid these dataset requirements, we attempted to use an unsupervised CycleGAN pix2pix model(5,6) to turn unpaired, unstained bright-field images into pathologist-approved digitally ‘stained’ images. Using formalin-fixed-paraffin-embedded liver samples, 5µm section images (20x) were obtained before and after staining to create “stained” an “unstained” datasets. Model implementation was conducted using Ubuntu 20.04.4 LTS, 32 GB RAM, Intel Core i7-9750 CPU @2.6 GHz, Nvidia GeForce RTX 2070 Mobile, Python 3.7.11 and Tensorflow 2.9.1. The CycleGAN framework utilized a u-net-based generator and discriminator from pix2pix, a CGAN. The CycleGAN used a modified loss function, cycle consistent loss that assumed unpaired images, so loss was measured twice. To our knowledge, this is the first documented application of this architecture using unpaired bright-field images. Results and suggested improvements are discussed.

A Data Augmentation Method for Motor Imagery EEG Signals Based on DCGAN-GP Network.

Motor imagery electroencephalography (EEG) signals have garnered attention in brain-computer interface (BCI) research due to their potential in promoting motor rehabilitation and control. However, the limited availability of labeled data poses challenges for training robust classifiers. In this study, we propose a novel data augmentation method utilizing an improved Deep Convolutional Generative Adversarial Network with Gradient Penalty (DCGAN-GP) to address this issue. We transformed raw EEG signals into two-dimensional time-frequency maps and employed a DCGAN-GP network to generate synthetic time-frequency representations resembling real data. Validation experiments were conducted on the BCI IV 2b dataset, comparing the performance of classifiers trained with augmented and unaugmented data. Results demonstrated that classifiers trained with synthetic data exhibit enhanced robustness across multiple subjects and achieve higher classification accuracy. Our findings highlight the effectiveness of utilizing a DCGAN-GP-generated synthetic EEG data to improve classifier performance in distinguishing different motor imagery tasks. Thus, the proposed data augmentation method based on a DCGAN-GP offers a promising avenue for enhancing BCI system performance, overcoming data scarcity challenges, and bolstering classifier robustness, thereby providing substantial support for the broader adoption of BCI technology in real-world applications.

Garbage In [formula omitted] Garbage Out: Exploring GAN Resilience to Image Training Set Degradations

Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance (FID) of images generated by a conditional deep convolutional GAN (cDCGAN). We find that at the α=0.05 level, brightening, darkening, and blurring are statistically significantly more detrimental to the resulting GAN image quality than removing the degraded data completely, while other degradations are typically safe to keep in training datasets. Additionally, we find that in the case of randomized partial occlusion, the FID of the resulting GAN images approaches that of the degraded training set for increasing levels of occlusion, with the surprising result that GAN FID performance is equal to that of the training set at 75% degradation.

Data augmentation-based enhanced fingerprint recognition using deep convolutional generative adversarial network and diffusion models

The progress of fingerprint recognition applications encounters substantial hurdles due to privacy and security concerns, leading to limited fingerprint data availability and stringent data quality requirements. This article endeavors to tackle the challenges of data scarcity and data quality in fingerprint recognition by implementing data augmentation techniques. Specifically, this research employed two state-of-the-art generative models in the domain of deep learning, namely Deep Convolutional Generative Adversarial Network (DCGAN) and the Diffusion model, for fingerprint data augmentation. Generative Adversarial Network (GAN), as a popular generative model, effectively captures the features of sample images and learns the diversity of the sample images, thereby generating realistic and diverse images. DCGAN, as a variant model of traditional GAN, inherits the advantages of GAN while alleviating issues such as blurry images and mode collapse, resulting in improved performance. On the other hand, Diffusion, as one of the most popular generative models in recent years, exhibits outstanding image generation capabilities and surpasses traditional GAN in some image generation tasks. The experimental results demonstrate that both DCGAN and Diffusion can generate clear, high-quality fingerprint images, fulfilling the requirements of fingerprint data augmentation. Furthermore, through the comparison between DCGAN and Diffusion, it is concluded that the quality of fingerprint images generated by DCGAN is superior to the results of Diffusion, and DCGAN exhibits higher efficiency in both training and generating images compared to Diffusion.

Investigation of generative capacity related to DCGANs across varied discriminator architectures and parameter counts: A comparative study

Generating lifelike images through generative models poses a significant challenge, where Generative Adversarial Networks (GANs), particularly Deep Convolutional GANs (DCGANs), are commonly employed for image synthesis. This study focuses on altering the DCGAN discriminators structure and parameter count, investigating their effects on the characteristics of the resulting generated images. Assessment of these models is carried out using the Frchet Inception Distance (FID) score, a metric that gauges the quality of generated image samples. The research specifically involves substituting some convolutional layers with fully-connected layers, and the ensuing outcomes are thoroughly compared to discern the impact of these structural changes. Furthermore, dropout was used to study the number of the parameters influence. This study compared the FID score of the models when the probability is 0, 0.2, 0.4, 0.6 and 0.8. Experimental results showed that the DCGAN with the fully-connected layers generated ability was stronger than the original one. Besides, when the probability of the dropout is 0.6, the images generated was the most realistic. Finally, the paper explained the possible reasons for the difference and proposed a better generative model based on DCGAN.

An online color and shape integrated detection method for flexible packaging surface defects

It is difficult for the spectrophotometer to meet the requirement of real-time color defect detection for flexible packaging prints. The false of shape defect detection is caused by artifact interference and insufficient classification accuracy of defect classification network. A color defect detection method for flexible packaging is proposed, which realizes the adaptive adjustment of the correction parameters of the Commission Internationale de l´Eclairage Delta E 2000 (CIEDE2000) equations with the detection object. It improves the speed and accuracy of the color defect detection for flexible packaging. An quadratic difference strategy is designed for template matching subtraction method to remove artifact interference. A method for enhancing shape defect data set of flexible packaging is proposed. Using discrete images of defects as network input, self-attention mechanism and spectral normalization methods are added to the deep convolutional generative adversarial networks (DCGAN) to enhance the effective dataset for the training of defect classification network. The accuracy of color detection for flexible packaging prints is improved by 38.7% based on optimized CIEDE2000. The average structure similarity index measure (SSIM) value of the improved DCGAN for defect detection is 0.845, and the Fréchet inception distance (FID) is 121.463. It takes 83.63 ms for the color and shape integrated detection method to detect shape defects on flexible packaging surfaces with an accuracy of 98.3%. The online color and shape integrated detection method can be applied to automated flexible packaging workshops to achieve real-time defect detection.

Near-infrared spectroscopy combined with deep convolutional generative adversarial network for prediction of component content in melt-cast explosive

Rapid and nondestructive prediction of component content is the key to improve industrial production efficiency. However, limited data sets also result in low generalization capabilities of the model, and it is time-consuming to obtain a large amount of content reference values and costly. Here, near infrared (NIR) spectroscopy technique combined with deep convolutional generated countermeasure network (DCGAN) was used to predict the trinitrotoluene (TNT) content of the melt-cast explosive. DCGAN was used to simultaneously extend its spectral data and content data. After several iterations, fake data were produced, which was very similar to the experimental data. The partial least squares (PLS) regression model was established and the performance was compared before and after data enhancement. The results showed that this method not only improved the performance of regression model, but also solved the problem of requiring large number of training data.

Generative Adversarial Networks-based solution for improving medical data quality and insufficiency

As big data brings intelligent solutions and innovations to various fields, the goal of this research is to solve the problem of poor-quality and insufficient datasets in the medical field, to help poor areas can access to high quality and rich medical datasets as well. This study focuses on solving the current problem by utilizing variants of generative adversarial network, Super Resolution Generative Adversarial Network (SRGAN) and Deep Convolutional Generative Adversarial Network (DCGAN). In this study, OpenCV is employed to introduce fuzziness to the Brain Tumor MRI Dataset, resulting in a blurred dataset. Subsequently, the research utilizes both the unaltered and blurred datasets to train the SRGAN model, which is then applied to enhance the low-quality dataset through inpainting. Moving forward, the original dataset, low-quality dataset, and the improved dataset are each used independently to train the DCGAN model. In order to compare the difference between the produced image datasets and the real dataset, the FID Score is separately computed. The results of the study found that by training DCGAN with SRGAN repaired medical dataset, the naked eye can observe that the medical image dataset is significantly clearer and there is a reduction in Frchet Inception Distance (FID) Score. Therefore, by using SRGAN and DCGAN the current problem of low quality and small quantity of datasets in the medical field can be solved, which increase the potential possibilities of big data in artificial intelligence filed of medicine.

A study on ice resistance prediction based on deep learning data generation method

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship navigation when sailing in ice regions. This paper focuses on the design of a data generation deep learning model and an interpretable ice resistance prediction deep learning model. By selecting the appropriate ship, ice parameters and model experimental data to build the dataset, the influence of different data preprocessing methods on the model results are discussed. The processed data is expanded by Deep Convolutional Generative Adversarial Network (DCGAN). On this basis, a Graph Neural Network (GNN) is built to explain the distribution of network weights and the generalization effect of the model. The established model (GNN-DCGAN) and several ice resistance prediction methods are compared and analyzed. In addition, the design of polar ships needs to comprehensively consider the dynamic changes of ship design parameters and the mechanical properties of ice. In this paper, taking MT UIKKU as an example, the parameter sensitivity analysis is carried out to study the scope of application of the model and the contribution of different parameters. The results show that the DCGAN method can improve the prediction accuracy and application scope of GNN model. The predictions exhibit a high level of agreement with the experimental data, which can provide valuable references for the design of ship in ice regions.

Exploring the influence of generator channel number on the quality of anime-style portrait generation based on DCGAN

In the realm of contemporary image synthesis, this research delves into a crucial objective: exploring the connection between the quantity of generator channels and the production of anime-style portraits through Deep Convolutional Generative Adversarial Networks (DCGAN). Employing an extensive dataset of anime faces encompassing diverse artistic styles, this study systematically examines the nuanced interplay between architectural parameters and the fidelity and intricacy of the generated images. By employing the Frechet Inception Distance (FID) as a metric for image quality, this investigation contributes significantly to the field by enhancing the understanding of how the number of generator channels impacts the ultimate quality of anime-style portraits. The DCGAN framework, and in particular its variants, is the backbone of this investigation. The generator and discriminator components are involved in adversarial training, a competitive process that improves image quality through iterations. The findings reveal a non-linear relationship between the number of generator channels and image quality. While increasing the number of channels initially improves image quality and decreases the FID value, exceeding the optimal threshold leads to diminishing returns and image quality degradation. The intricate interplay between structure selection and image quality is further confirmed by the dynamics of the generator and discriminator loss functions. By elucidating the trade-off between complexity and image fidelity, this study contributes to the advancement of image synthesis techniques and encourages future exploration of architectural nuances in the field of artistic image generation.

The Effect of Latent Space Vector on Generating Animal Faces in Deep Convolutional GAN: An Analysis

Researchers are showing great interest in Generative Adversarial Networks (GANs), which use deep learning techniques to mimic the content of datasets and are particularly adept at data generation. Despite their impressive performance, there is uncertainty about how GANs precisely map latent space vectors to realistic images and how the chosen dimensionality of the latent space affects the quality of the generated images. In this paper, we explored the potential of generative models in generating animal face images. For this purpose, we used the Deep Convolutional Generative Adversarial Network (DCGAN) model as a reference. To analyze the impact of selected latent space vectors, we synthesized animal face images by training data representations in the DCGAN model with the well-known AFHQ dataset from the literature. We compared the quantitative evaluation of the produced images using Fréchet Inception Distance (FID) and Inception Score (IS). As a result, we demonstrated that generative models can produce images with latent sizes significantly smaller and larger than the standard size of 100.