Weakly Supervised Object Discovery by Generative Adversarial & Ranking Networks

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In‐depth analysis of deep generative adversarial network applications, namely, image‐to‐image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam‐based eye trackers, lumen center detection, query‐by‐string word, intermittently closed and open lakes and lagoons, and landslides.

The effectiveness of detecting malicious files heavily relies on the quality of the training dataset, particularly its size and authenticity. However, the lack of high-quality training data remains one of the biggest challenges in achieving widespread adoption of malware detection by trained machine and deep learning models. In response to this challenge, researchers have made initial strides by employing generative techniques to create synthetic malware samples. This work utilizes deep variational autoencoders (VAE) and generative adversarial networks (GAN) to produce malware samples as opcode sequences. The generated malware opcodes are then distinguished from authentic opcode samples using machine and deep learning techniques as validation methods. The primary objective of this study was to compare synthetic malware generated using VAE and GAN technologies. The results showed that neither approach could create synthetic malware that could deceive machine learning classification. However, the WGAN-GP algorithm showed more promise by requiring a higher number of synthetic malware samples in the train set to effectively be detected, proving ita better approach in synthetic malware generation.

Generative models rely on the idea that data can be represented in terms of latent variables which are uncorrelated by definition. Lack of correlation among the latent variable support is important because it suggests that the latent-space manifold is simpler to understand and manipulate than the real-space representation. Many types of generative model are used in deep learning, e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs). Based on the idea that the latent space behaves like a vector space Radford et al. (2015), we ask whether we can expand the latent space representation of our data elements in terms of an orthonormal basis set. Here we propose a method to build a set of linearly independent vectors in the latent space of a trained GAN, which we call quasi-eigenvectors. These quasi-eigenvectors have two key properties: i) They span the latent space, ii) A set of these quasi-eigenvectors map to each of the labeled features one-to-one. We show that in the case of the MNIST image data set, while the number of dimensions in latent space is large by design, 98% of the data in real space map to a sub-domain of latent space of dimensionality equal to the number of labels. We then show how the quasi-eigenvectors can be used for Latent Spectral Decomposition (LSD). We apply LSD to denoise MNIST images. Finally, using the quasi-eigenvectors, we construct rotation matrices in latent space which map to feature transformations in real space. Overall, from quasi-eigenvectors we gain insight regarding the latent space topology.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs) provides a new method and model for computer vision. The idea of GANs using the game training method is superior to traditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in image generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some problems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs and surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in computer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest research progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. The future development of GANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.

We combine inverse autoregressive flows (IAF) and variational Bayesian inference (variational Bayes) in the context of geophysical inversion parameterized with deep generative models encoding complex priors. Variational Bayes approximates the unnormalized posterior distribution parametrically within a given family of distributions by solving an optimization problem. Although prone to bias if the chosen family of distributions is too limited, it provides a computationally-efficient approach that scales well to high-dimensional inverse problems. To enhance the expressiveness of the variational distribution, we explore its combination with IAFs that allow samples from a simple base distribution to be pushed forward through a series of invertible transformations onto an approximate posterior. The IAF is learned by maximizing the lower bound of the evidence (marginal likelihood), which is equivalent to minimizing the Kullback–Leibler divergence between the approximation and the target posterior distribution. In our examples, we use either a deep generative adversarial network (GAN) or a variational autoencoder (VAE) to parameterize complex geostatistical priors. Although previous attempts to perform Gauss–Newton inversion in combination with GANs of the same architecture were proven unsuccessful, the trained IAF provides a good reconstruction of channelized subsurface models for both GAN- and VAE-based inversions using synthetic crosshole ground-penetrating-radar data. For the considered examples, the computational cost of our approach is seven times lower than for Markov chain Monte Carlo (MCMC) inversion. Furthermore, the VAE-based approximations in the latent space are in good agreement. The VAE-based inversion requires only one sample to estimate gradients with respect to the IAF parameters at each iteration, while the GAN-based inversions need more samples and the corresponding posterior approximation is less accurate.

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1,024 × 1,024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological modeling and research.

High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.

Structural geological modeling is aimed at finding a representation of geological units. This is a complex ill-posed problem, and the data may be sparse and of varying quality, leading to multiple geological models consistent with them. Despite continuous advances for decades in geological modeling, recent studies still show some unresolved industrial challenges. Consequently, this work aims to improve geological modeling by proposing a novel approach with a focus on improving uncertainty management. A stochastic approach is developed based on deep generative methods, namely generative adversarial networks (GANs). Thanks to a synthetic dataset, a GAN is trained to generate two-dimensional unconditional geomodels that are plausible. Subsequently, a Bayesian inversion is performed with a Metropolis-adjusted Langevin algorithm (MALA) to produce geomodels consistent with field data. The proposed approach is validated on different conditioning data. For each case, the approach is able to successfully produce a variety of geomodels that can be linked to different geological settings. The uncertainties on geological units are measured by Shannon entropy on the generated models.

This review paper presents a comprehensive analysis of electroluminescence (EL) imaging techniques for photovoltaic (PV) module diagnostics, focusing on advancements from conventional indoor imaging to outdoor and daylight EL imaging. It examines key challenges, including ambient light interference and environmental variability, and highlights innovations such as infrared-sensitive indium gallium arsenide (InGaAs) cameras, optical filtering, and periodic current modulation to enhance defect detection. The review also explores the role of artificial intelligence (AI)-driven methodologies, including deep learning and generative adversarial networks (GANs), in automating defect classification and performance assessment. Additionally, the emergence of drone-based EL imaging has facilitated large-scale PV inspections with improved efficiency. By synthesizing recent advancements, this paper underscores the critical role of EL imaging in ensuring PV module reliability, optimizing performance, and supporting the long-term sustainability of solar energy systems.

Recent advances in deep convolutional neural networks (DCNNs) and generative adversarial networks (GANs) have significantly improved the performance of single image blind deblurring algorithms. However, most of the existing algorithms require paired training data. In this paper, we present an unsupervised method for single-image deblurring without paired training images. We introduce a disentangled framework to split the content and blur features of a blurred image, which yields improved deblurring performance. To handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the restored results match the original images. We also add a perceptual loss to further mitigate the artifacts. For natural image deblurring, we introduce a color loss to reduce color distortions in outputs. Extensive experiments on both domain-specific and natural image deblurring show the proposed method achieves competitive results compared to recent state-of-the-art deblurring approaches.

Tourism is one of the most important sectors of the global economy, generating billions of euros in revenue and supporting millions of jobs. However, the tourism industry also faces many challenges, such as changing consumer preferences, environmental impacts and competitive pressures. In this paper, we explore how specialized and generative AI, a branch of artificial intelligence that can create novel content, such as text, images, or audio, can support hotels and other tourist accommodation companies in enhancing their products and services, improving their marketing and communication strategies and optimizing their operations and management. We review the existing literature on AI and its applications in tourism and propose potential use cases of generative AI for tourist companies. We also present some examples of generative AI tools that can be used for these purposes and discuss their benefits and limitations. Finally, we offer some conclusions and future implications for research and practice in this emerging field. Theoretical implications of the present paper suggest that the incorporation of generative AI in the tourism sector can significantly advance the current understanding of AI applications in service industries. By leveraging machine learning techniques, such as deep neural networks and generative adversarial networks, the tourism industry can create novel content and optimize various operations. This study enriches existing literature by identifying new use cases for generative AI, thus providing a foundation for further academic inquiry. Practically, our findings reveal several actionable insights for tourism companies. Generative AI tools can be harnessed to personalize customer experiences, enhance marketing efforts, and streamline operational management.

An improved DualGAN for near-infrared image colorization

Unlike standard Convolutional Neural Networks (CNNs) with fully-connected layers, Fully Convolutional Neural Networks (FCN) are prevalent in computer vision applications such as object detection, semantic/image segmentation, and the most popular generative tasks based on Generative Adversarial Networks (GAN). In an FCN, traditional convolutional layers and deconvolutional layers contribute to the majority of the computation complexity. However, prior deep learning accelerator designs mostly focus on CNN optimization. They either use independent compute-resources to handle deconvolution or convert deconvolutional layers (Deconv) into general convolution operations, which arouses considerable overhead. To address this problem, we propose a unified fully convolutional accelerator aiming to handle both the deconvolutional and convolutional layers with a single processing element (PE) array. We re-optimize the conventional CNN accelerator architecture of regular 2D processing elements array, to enable it more efficiently support the data flow of deconvolutional layer inference. By exploiting the locality in deconvolutional filters, this architecture reduces the consumption of on-chip memory communication from 24.79 GB to 6.56 GB and improves the power efficiency significantly. Compared to prior baseline deconvolution acceleration scheme, the proposed accelerator achieves 1.3X -- 44.9X speedup and reduces the energy consumption by 14.6%-97.6% on a set of representative benchmark applications. Meanwhile, it keeps similar CNN inference performance to that of an optimized CNN-only accelerator with negligible power consumption and chip area overhead.

Fast transformation of discriminators into encoders using pre-trained GANs