Generative Adversarial Networks Research Articles

Tackling Class-imbalanced Learning Issues Based on Local Neighborhood Information and Generative Adversarial Networks

Tackling Class-imbalanced Learning Issues Based on Local Neighborhood Information and Generative Adversarial Networks

Image-based rice leaf disease detection using CNN and generative adversarial network

Image-based rice leaf disease detection using CNN and generative adversarial network

An Intelligent Kick Detection Model for Large-Hole Ultra-Deep Wells in the Sichuan Basin

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

Safeguarding User-Centric Privacy in Smart Homes

Internet of Things (IoT) devices have been increasingly deployed in smart homes to automatically monitor and control their environments. Unfortunately, extensive recent research has shown that on-path external adversaries can infer and further fingerprint people’s sensitive private information by analyzing IoT network traffic traces. In addition, most recent approaches that aim to defend against these malicious IoT traffic analytics cannot adequately protect user privacy with reasonable traffic overhead. In particular, these approaches often did not consider practical traffic reshaping limitations, user daily routine permitting, and user privacy protection preference in their design. To address these issues, we design a new low-cost, open source user-centric defense system—PrivacyGuard—that enables people to regain the privacy leakage control of their IoT devices while still permitting sophisticated IoT data analytics that is necessary for smart home automation. In essence, our approach employs intelligent deep convolutional generative adversarial network assisted IoT device traffic signature learning, long short-term memory based artificial traffic signature injection, and partial traffic reshaping to obfuscate private information that can be observed in IoT device traffic traces. We evaluate PrivacyGuard using IoT network traffic traces of 31 IoT devices from five smart homes and buildings. We find that PrivacyGuard can effectively prevent a wide range of state-of-the-art adversarial machine learning and deep learning based user in-home activity inference and fingerprinting attacks and help users achieve the balance between their IoT data utility and privacy preserving.

DEGAN: Detail-Enhanced Generative Adversarial Network for Monocular Depth-Based 3D Reconstruction

Although deep networks-based 3D reconstruction methods can recover the 3D geometry given few inputs, they may produce unfaithful reconstruction when predicting occluded parts of 3D objects. To address the issue, we propose Detail-Enhanced Generative Adversarial Network (DEGAN) which consists of Encoder–Decoder-Based Generator (EDGen) and Voxel-Point Embedding Network-Based Discriminator (VPDis) for 3D reconstruction from a monocular depth image of an object. Firstly, EDGen decodes the features from the 2.5D voxel grid representation of an input depth image and generates the 3D occupancy grid under GAN losses and a sampling point loss. The sampling loss can improve the accuracy of predicted points with high uncertainty. VPDis helps reconstruct the details under voxel and point adversarial losses, respectively. Experimental results show that DEGAN not only outperforms several state-of-the-art methods on both public ModelNet and ShapeNet datasets but also predicts more reliable occluded/missing parts of 3D objects.

Lateritic Ni–Co Prospectivity Modeling in Eastern Australia Using an Enhanced Generative Adversarial Network and Positive-Unlabeled Bagging

AbstractThe surging demand for Ni and Co, driven by the acceleration of clean energy transitions, has sparked interest in the Lachlan Orogen of New South Wales for its potential lateritic Ni–Co resources. Despite recent discoveries, a substantial knowledge gap exists in understanding the full scope of these critical metals in this geological province. This study employed a machine learning-based framework, integrating multidimensional datasets to create prospectivity maps for lateritic Ni–Co deposits within a specific Lachlan Orogen segment. The framework generated a variety of data-driven models incorporating geological (rock units, metamorphic facies), structural, and geophysical (magnetics, gravity, radiometrics, and remote sensing spectroscopy) data layers. These models ranged from comprehensive models that use all available data layers to fine-tuned models restricted to high-ranking features. Additionally, two hybrid (knowledge-data-driven) models distinguished between hypogene and supergene components of the lateritic Ni–Co mineral systems. The study implemented data augmentation methods and tackled imbalances in training samples using the SMOTE–GAN method, addressing common machine learning challenges with sparse training data. The study overcame difficulties in defining negative training samples by translating geological and geophysical data into training proxy layers and employing a positive and unlabeled bagging technique. The prospectivity maps revealed a robust spatial correlation between high probabilities and known mineral occurrences, projecting extensions from these sites and identifying potential greenfield areas for future exploration in the Lachlan Orogen. The high-accuracy models developed in this study utilizing the Random Forest classifier enhanced the understanding of mineralization processes and exploration potential in this promising region.

Kruskal Szekeres generative adversarial network augmented deep autoencoder for colorectal cancer detection.

Cancer involves abnormal cell growth, with types like intestinal and oesophageal cancer often diagnosed in advanced stages, making them hard to cure. Symptoms are like burning sensations in the stomach and swallowing difficulties are specified as colorectal cancer. Deep learning significantly impacts the medical image processing and diagnosis, offering potential improvements in accuracy and efficiency. The Kruskal Szekeres Generative Adversarial Network Augmented Deep Autoencoder (KSGANA-DA) is introduced for early colorectal cancer detection and it comprises two stages; Initial stage, data augmentation uses Affine Transform via Random Horizontal Rotation and Geometric Transform via Kruskal-Szekeres that coordinates to improve the training dataset diversity, boosting detection performance. The second stage, a Deep Autoencoder Anatomical Landmark-based Image Segmentation preserves edge pixel spatial locations, improving precision and recall for early boundary detection. Experiments validate KSGANA-DA performance and different existing methods are implemented into Python. The results of KSGANA-DA are to provide higher precision by 41%, recall by 7%, and lesser training time by 46% than compared to conventional methods.

Fire image enhancement method based on generative adversarial networks for improving fire detection performance through cameras

Fire image enhancement method based on generative adversarial networks for improving fire detection performance through cameras

The Use of Deepfake Technology in China: Problems of Legal Regulation and Ways to Solve Them

The core of the deepfake technology is based on a generative-adversarial network built on a combination of two neural networks: a generative network (network G) creates samples, a discriminative network (network D) tries to distinguish correct («genuine») samples from incorrect ones. Networks G and D compete with each other thousands or even millions of times until network G improves its performance. Thus, the network D will no longer be able to distinguish real data from fake data. With the development of big data and machine learning technologies, the scenario for using deepfake technology has gradually changed from creating sound models and imitating text to deep video forgery. For a long time, images modified using traditional Photoshop and other technologies were easily recognized. Deepfake technology changed this situation, making it more difficult to identify fakes. As an important technological innovation in the field of artificial intelligence, deepfake technology is widely used in various areas of society, creating enormous applied value. However, any technology is a double-edged sword. The use of deepfake technology poses a great threat to personal privacy, property security and even national security. In order to find a balance between technological innovation and risk prevention and control, countries around the world are actively exploring various ways to manage. The paper describes the main risks posed by modern deepfake technology, provides an overview of legal regulation in this area in China and offers an effective way to solve problems.

Optimizing Multi-Omics Data Imputation with NMF and GAN Synergy.

Integrating multiple omics datasets can significantly advance our understanding of disease mechanisms, physiology, and treatment responses. However, a major challenge in multi-omics studies is the disparity in sample sizes across different datasets, which can introduce bias and reduce statistical power. To address this issue, we propose a novel framework, OmicsNMF, designed to impute missing omics data and enhance disease phenotype prediction. OmicsNMF integrates Generative Adversarial Networks (GANs) with Non-Negative Matrix Factorization (NMF). NMF is a well-established method for uncovering underlying patterns in omics data, while GANs enhance the imputation process by generating realistic data samples. This synergy aims to more effectively address sample size disparity, thereby improving data integration and prediction accuracy. For evaluation, we focused on predicting breast cancer subtypes using the imputed data generated by our proposed framework, OmicsNMF. Our results indicate that OmicsNMF consistently outperforms baseline methods. We further assessed the quality of the imputed data through survival analysis, revealing that the imputed omics profiles provide significant prognostic power for both overall survival and disease-free status. Overall, OmicsNMF effectively leverages GANs and NMF to impute missing samples while preserving key biological features. This approach shows potential for advancing precision oncology by improving data integration and analysis. Source code is available at: https://github.com/compbiolabucf/OmicsNMF.

A Helium Speech Correction Method Based on Generative Adversarial Networks

The distortion of helium speech caused by helium−oxygen gas mixtures significantly impacts the safety and communication efficiency of saturation divers. Although existing correction methods have shown some effectiveness in improving the intelligibility of helium speech, challenges remain in enhancing clarity and high−pitch correction. To address the issue of degraded speech quality post−correction, a novel helium speech correction method based on generative adversarial networks (GANs) is proposed. Firstly, a new helium speech dataset is introduced, which includes isolated words and continuous speech in both Chinese and English. By training and testing on both isolated words and continuous passages, the correction capability of the model can be accurately evaluated. Secondly, a new evaluation system for helium speech correction is proposed, which partially fills the gap in current helium speech evaluation metrics. This system uses comprehensive similarity to evaluate the similarity of keywords at the sentence level, thus assessing the correction results of helium speech from both word and sentence dimensions. Lastly, a GAN−based helium speech correction method is designed. This method solves the problems of pitch period distortion and formant shift in helium speech by introducing an adaptive speech segmentation algorithm and a fusion loss function and significantly improves the clarity and intelligibility of corrected helium speech. The experimental results show that the corrected helium speech is improved in clarity and intelligibility, which shows its practical value and application potential.

Controllable Game Level Generation: Assessing the Effect of Negative Examples in GAN Models

Generative Adversarial Networks (GANs) are unsupervised models designed to learn and replicate a target distribution. The vanilla versions of these models can be extended to more controllable models. Conditional Generative Adversarial Networks (CGANs) extend vanilla GANs by conditioning both the generator and discriminator on some additional information (labels). Controllable models based on complementary learning, such as Rumi-GAN, have been introduced. Rumi-GANs leverage negative examples to enhance the generator's ability to learn positive examples. We evaluate the performance of two controllable GAN variants, CGAN and Rumi-GAN, in generating game levels targeting specific constraints of interest: playability and controllability. This evaluation is conducted under two scenarios: with and without the inclusion of negative examples. The goal is to determine whether incorporating negative examples helps the GAN models avoid generating undesirable outputs. Our findings highlight the strengths and weaknesses of each method in enforcing the generation of specific conditions when generating outputs based on given positive and negative examples.

The Impact of AI and VR/AR Technologies on Yuan Blue-and-White Porcelain Art

Yuan blue-and-white porcelain, a treasure of Chinese ceramic art, is renowned worldwide for its unique artistic value and cultural significance. With the continuous advancement of artificial intelligence (AI) technology, the artistic innovation of Yuan blue-and-white porcelain is experiencing new opportunities. AI technology not only improves the efficiency and quality of creation but also breaks through traditional physical limitations, enabling the creation of more diverse and creative artworks. By integrating augmented reality (AR) and virtual reality (VR) technologies, AI provides new ways to display and interact with Yuan blue-and-white porcelain art. In non-physical artistic innovation, AI generates new designs through deep learning algorithms, breaking free from the constraints of traditional handmade processes and creating diverse works. Technologies like generative adversarial networks (GANs) can produce highly realistic Yuan blue-and-white designs, while convolutional neural networks (CNNs) can quickly generate design sketches, enhancing creative efficiency. AI can also optimize designs, improving the artistic quality and visual effects of the works.AR and VR technologies offer immersive experiences and interactivity for the audience. Through virtual exhibition spaces and AR applications, viewers can appreciate Yuan blue-and-white artworks as if they were physically present, enhancing interactivity and visual appeal. VR technology, through virtual exhibitions and other means, enhances the viewing experience and impact of the artworks. The application of AI technology in the non-physical artistic innovation of Yuan blue-and-white porcelain not only enhances creation efficiency and quality but also brings limitless possibilities to artistic creation. The combination of AR and VR technologies further enhances the visual appeal and interactivity of Yuan blue-and-white art. As technology continues to advance, AI will play an increasingly important role in the artistic innovation of Yuan blue-and-white porcelain, creating more splendid artworks.

Enhancing Text-to-Image Generation: Integrating CLIP and Diffusion Models for Improved Visual Accuracy and Semantic Consistency

Abstract. Text-to-Image (T2I) generation focuses on producing images that precisely match given textual descriptions by combining techniques from computer vision and natural language processing (NLP). Existing studies have shown an innovative approach to enhance T2I generation by integrating Contrastive Language-Image Pretraining (CLIP) embeddings with a Diffusion Model (DM). The method involves initially extracting rich and meaningful text embeddings using CLIP, which are then transformed into corresponding images. These images are progressively refined through an iterative denoising process enabled by diffusion models. Comprehensive experiments conducted on the MS-COCO dataset validate the proposed method, demonstrating significant improvements in image fidelity and the alignment between text and images. When compared to traditional models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which often struggle with maintaining both visual quality and semantic accuracy, this hybrid model shows superior performance. Future research could explore optimizing hybrid models further and applying T2I technology to specialized fields, such as medical imaging and scientific visualization, expanding its potential use cases.

Stock market prediction using Generative Adversarial Network (GAN) – Study case Germany stock market

Using neural networks in economics time series data is a new unexplored field. Lot of companies and economics research use mostly logistic regression or statistical approach when they try to predict the movement of stock market. Neural networks became a frequent tool for prediction in recent years and this approach has been confirmed to provide more reliable and better solutions when it comes to prediction and accuracy power. Within a wider context of current debate on neural networks employment in stock market predictions, we suggest an innovative methodology based on the combination of neural networks. In our analysis we use Wasserstein Generative Adversarial Network (WGAN) on Germany stock market as an example. We present how the trading strategy could be established on the prediction of the model and how it can be compared with other models in terms of returns. Overall, the WGAN monthly prediction outperformed Random Forest by 36%, benchmark by 32% and LSTM by 26% in the testing period. Our results also suggest that the WGAN model has on average higher returns than pure investment into index. Furthermore, WGAN is less volatile, which is always the preferred option for investors. Using neural networks for stock index prediction and confirming that WGAN investment strategy brings higher returns compared to generally used models is our main contribution to the current debate.

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.

To accurately capture an individual's food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community. This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

An innovative framework to securely transfer data through the Internet of Things using advanced generative adversarial networks

The Internet of Things (IoT) is developing so quickly that cloud-centric computing finds it challenging to keep up with the demands of usability and low latency. Edge computing unifies computing, networking, storage and applications into a distributed open system. At the edge of the IoT, it provides intelligent services. The edge network is made up of several wired and wireless networks, and edge nodes have constrained amounts of memory and processing power. The edge network is vulnerable to several types of cyberattacks because of these factors. Large-scale network data gathering and detection for IoT security is also challenging for an IoT edge node to provide. Data analytics for intrusion detection guarantees high accuracy of intrusion detection systems (IDSs), but the implementation of such algorithms on IoT might be a challenge due to the limited resources on edge nodes. Inspired in part by these challenges, we suggest a sophisticated IDS based on a generative adversarial network (GAN). This article suggests a novel method for detecting intrusions in the IoTs networks that make use of Colony Predator Algorithm (CPA) for optimization of the detection process. Through the use of GANs to create real-looking data samples, the suggested technique helps to have reliable anomaly and intrusion detection. The integration improves the training process, making it more efficient and enabling the CPA system to better differentiate between benign and malicious activities, which exponentially raises the system's efficiency.

Generative AI for Consumer Behavior Prediction: Techniques and Applications

Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, have revolutionized consumer behavior prediction by enabling the synthesis of realistic data and extracting meaningful insights from large, unstructured datasets. However, despite their potential, the effectiveness of these models in practical applications remains inadequately addressed in the existing literature. This study aims to investigate how generative AI models can effectively enhance consumer behavior prediction and their implications for real-world applications in marketing and customer engagement. By systematically reviewing 31 studies focused on these models in e-commerce, energy data modeling, and public health, we identify their contributions to improving personalized marketing, inventory management, and customer retention. Specifically, transformer models excel at processing complicated sequential data for real-time consumer insights, while GANs and VAEs are effective in generating realistic data and predicting customer behaviors such as churn and purchasing intent. Additionally, this review highlights significant challenges, including data privacy concerns, the integration of computing resources, and the limited applicability of these models in real-world scenarios.

A Comparative Analysis of StackGAN and AttnGAN in Text-to-Image Generation

Abstract. This research looks at text-to-image generation as a whole, comparing two popular modelsStacked Generative Adversarial Networks (StackGAN) and Attentional Generative Adversarial Networks (AttnGAN)and their respective strengths and weaknesses. Text-to-image generation has seen significant advancements with the introduction of GAN-based models, and this paper aims to explore how these models perform in terms of image quality, realism, and alignment with textual descriptions. Using the Caltech-UCSD Birds (CUB)-200-2011 dataset, which consists of bird images, extensive experiments were conducted to evaluate and compare the capabilities of the two models. The results indicate that AttnGAN outperforms StackGAN across multiple metrics, particularly in the accuracy of detail alignment and overall image realism. AttnGAN's multi-level attention mechanism allows it to pay attention to specific textual elements when generating related sections of the image, resulting in more aesthetically pleasing and semantically consistent outputs. Despite these advancements, challenges remain in improving both the diversity and quality of generated images. This work offers substantial insights into the capabilities and constraints of existing models, providing guidance for future research with the aim of improving text-to-image generation.

A computationally efficient method for induction motor bearing fault detection based on parallel convolutions and semi-supervised GAN

ABSTRACT Accurate and timely bearing fault detection is imperative for optimal system functioning and the implementation of preventative maintenance measures. Deep learning models provide viable solutions to these malfunctions, however, the lack of labelled data makes the training both expensive and cumbersome. To remedy this, various semi-supervised approaches have surfaced in the last decade, significantly mitigating the need for extensive labelled data but with added computational cost. This study proposes one such approach by leveraging generative adversarial networks (GAN) trained on a time-frequency based representation. The proposed Parallel Convolutions Semi-Supervised GAN, namely PC-SSGAN, uses bottleneck parallel convolutions blocks to capture multi-scale features in both local and global contexts, lacing both the generator and discriminator with enhanced feature extraction capabilities and simultaneously reducing the parameters and training time. The Proposed framework is evaluated on two distinct open-source datasets. The classification accuracy for both models exceeded 99.50%. Moreover, the proposed parallel convolutions-based architecture spent approximately 33% less time on training than the normal convolutional layers. It has been foreseen that the proposed fault detection system can be integrated into the motor fault tolerant control system to produce a unified framework that can make informed decisions to handle the bearing faults effectively.