Extending Output Attentions in Recurrent Neural Networks for Dialog Generation

Generative networks are effective tools for digital materials (DM) inverse design. However, the optimization performance of generative networks is restricted by the increasing discrepancy between the optimized input and the prescribed input domain as the design loop increases. Herein, a correction technique is incorporated into generative deep neural network (GDNN) and generative deep convolutional neural network (GDCNN). The correction is performed by pulling the machine learning (ML)‐optimized inputs back to the prescribed domain at certain interval during the optimization process instead of only postprocessing at the end. A DM system with two phases, i.e., the matrix phase and the pore phase, is used for the structural design. The datasets are produced using a numerical model and describe the relationship between the material structure and the elastic modulus and tensile strength. The results show that the optimization effectiveness of corrected GDNN/GDCNN significantly improves given the fact that more structures converge to the best structures and fewer nonrepetitive structures are left after optimization, which helps to search for the best structures and decreases the computational burden when verifying the ML‐recommended structures. The corrected GDNN and GDCNN also manage to find structures with higher tensile strength in the new inverse design.

Large amount of annotated images with rich variations are needed to train a deep network for detecting instance object in unstructured environment. Addressing the problem that the artificial acquisition and manual annotation is time-consuming, the generative deep deconvolutional neural network (GDDNE) to increase and diversify training data through the supervised learning strategy is created in this paper. Specifically, our network can not only generate with different styles such as shift, zoom, brightness and other superimposed transformations, but also interpolate generate the new ones between given viewpoints images in training samples. With 180 viewpoints realistic images in training samples: 30 rotation angles in plane and 6 angles of depression, our network can finally generated 1000 diversified viewpoint images and 21 kinds of data transformations for each instance object. Abundant experiments demonstrate that the remarkable performance of our generative network used in the generation task of large magnitude.

<span lang="EN-IN">There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.</span>

With the rapid development of neural networks, many deep learning-based image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning-based approach for image image denoising. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on image denoising task on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against current state-of-the-art approaches.

Image style transformation techniques are an important research area in the field of computer vision, aiming to combine the content of one image with the artistic style of another image to create new images with the content of the input image and the style of the artistic style image. The development of this field has benefited from the rapid development of deep learning algorithms, especially the application of techniques such as Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). At the heart of image style transformation is how to capture both the content and artistic style of the input image. This is typically achieved by using different levels of activation of the convolutional neural network as content and style representations. The content representation captures the object and structural information in the image, while the style representation captures the texture and colour information of the image. To capture the style of an image, a Gram matrix is typically used to measure the correlation between features at a particular level. This matrix representation allows us to capture texture information and colour distribution, thus enabling style migration. The goal of the image style transformation model is to minimise both content loss and style loss to produce synthetic images. Content loss typically uses Euclidean distance to measure the difference between content representations, while style loss involves the distance between Gram matrices. GANs have become an important tool in image style transformation. The generator network is responsible for generating synthetic images and the discriminator network evaluates the realism of the generated images. Through adversarial training, the generator and discriminator networks constantly compete to improve the quality of the generated images. Finally, this study highlights the potential applications of image style conversion techniques in the fields of art creation, image editing and virtual reality, and proposes directions for future research on deep learning algorithms and underlying principles to further improve the efficiency and quality of image style conversion techniques. The continuous development of this field will bring new opportunities for image processing and creative applications.

Many machine learning models require huge datasets to get trained, to make correct predictions, or to increase their accuracy, that's where generative adversarial network (GAN) comes into the picture. GAN is a deep learning model, which can be used to create artificial data. It consists of two neural networks that cooperate in a way that resembles a game: a generator network and a discriminator network. Within the suggested model in this work, a dense motion network-based first-order motion model for image animation is developed. Here a trained GAN extracts the face landmarks from the driving video and develops the embedding model to create the synthesis video using the dedicated module to prepare the Deepfakes, employing key point detectors as a baseline. Lastly, an approach that makes use of dense motion networks to increase the effectiveness of a collection of GAN generators is provided. With the help of the sequel driving combination of driving video with the source image, the given results produce the augmented animation video. Hence, it is tried to implement a lighter model which consists of modules, with the approximate same accuracy when compared with other implementations of GAN. This work has a wide range of applications, including doubling dataset counts with a small number of sources, creating real-time backgrounds and characters for the gaming and animation industries using CG platforms, translating clothes, predicting videos, creating 3D objects, etc.

Low-level image processing is mainly concerned with extracting descriptions (that are usually represented as images themselves) from images. With the rapid development of neural networks, many deep learning-based low-level image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning based approach for low-level image processing, in particular, image denoising, image deblurring, and compressed image restoration. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a multi-objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on three low-level image processing tasks on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against all current state-of-the-art approaches.

In this paper, a deep learning (DL) framework was proposed to predict the taxi-passenger demand while the spatial, the temporal, and external dependencies were considered simultaneously. The proposed DL framework combined a modified density-based spatial clustering algorithm with noise (DBSCAN) and a conditional generative adversarial network (CGAN) model. More specifically, the modified DBSCAN model was applied to produce a number of sub-networks considering the spatial correlation of taxi pick-up events in the road network. And the CGAN model, fed with the historical taxi passenger demand and other conditional information, was capable to predict the taxi-passenger demands. The proposed CGAN model was made up with two long short-term memory (LSTM) neural networks, which are termed as the generative network ${G}$ and the discriminative network ${D}$ , respectively. Adversarial training process was conducted to the two LSTMs. In the numerical experiment, different model layouts were compared. It was found that different network layouts provided reasonable accuracy. With limited training data, more LSTM layers in the generator network resulted in not only higher accuracy, but also more difficulties in training. Comparisons were also conducted between the proposed prediction model and four typical approaches, including the moving average method, the autoregressive integrated moving method, the neural network model, and the LSTM neural network model. The comparison results showed that the proposed model outperformed all the other methods. And the repeated experiment indicated that the proposed CGAN model provided significant better predictions than the LSTM model did. Future research was recommended to include more datasets for testing the model and more information for improving predictive performance.

Interpreting seismic data requires the characterization of a number of key elements such as the position of faults and main reflections, presence of structural bodies, and clustering of areas exhibiting a similar amplitude versus angle response. Manual interpretation of geophysical data is often a difficult and time-consuming task, complicated by lack of resolution and presence of noise. In recent years, approaches based on convolutional neural networks have shown remarkable results in automating certain interpretative tasks. However, these state-of-the-art systems usually need to be trained in a supervised manner, and they suffer from a generalization problem. Hence, it is highly challenging to train a model that can yield accurate results on new real data obtained with different acquisition, processing, and geology than the data used for training. In this work, we introduce a novel method that combines generative neural networks with a segmentation task in order to decrease the gap between annotated training data and uninterpreted target data. We validate our approach on two applications: the detection of diffraction events and the picking of faults. We show that when transitioning from synthetic training data to real validation data, our workflow yields superior results compared to its counterpart without the generative network.

The problem and the aim of the study. Modernized higher education provides great prospects for artificial intelligence technologies to be introduced when training engineers for knowledge-intensive and high-tech industries. Applying generative neural networks makes it possible to meet the requirements both for programs to train relevant specialists and for the level of developed skills key to Industry 4.0 and for further Industry 5.0. The purpose of the study is to identify the possibilities of using generative neural networks when training digital engineers to improve the quality of their professional training. Research methods. The authors consider the generative neural network as an open resource with which it is possible to design, individualize and fill educational content in accordance with the professional interests of the participants in the interaction. The choice of service is justified by the type of information being processed and the engineer's work function being implemented. 68 bachelors of Vyatka State University are involved (Russian Federation). Training program: 08.03.01 – Construction. Focus: hydraulic engineering, industrial and civil engineering, Digital technologies for the examination of construction sites and real estate management. The assessment of the quality (level) of training is carried out with the help of materials from the fund of complex qualification tasks. For statistical processing of data, Pearson's chi-squared test is used. Results. The directions of work of students of the experimental group studying the discipline "Information Technology", including interactive communication supported by neural networks: generation of images and 3D-models, search for original titles, selection of a list of references, construction of diagrams and algorithms, etc. Statistically significant differences were revealed in the changes that occurred in the pedagogical system (χ2 = 9.095; p < 0.05). Conclusion. The summary are formulated that neural network services contribute to improving the quality of training due to the following didactic capabilities: presentation of information in various forms, automation of calculations, analysis of large amounts of data, decision support, etc. Difficulties are also highlighted: constantly updated features of services, the English-language interface, compliance with copyright and ethics of interaction on the network

The use of is widespread in the biological domain, and has also been used extensively by armed forces around the world in order to make visual detection and classification of objects of military interest more difficult. The recent advent of ever more autonomous military agents raises the questions of whether can have a similar effect on autonomous agents as it has on human agents, and if so, what kind of will be effective against such adversaries. In previous works, we have shown that image classifiers based on deep neural networks can be confused by patterns generated by generative networks (GANs). Specifically, we trained a classifier to distinguish between two ship types, military and civilian. We then used a GAN to generate patterns that, when overlaid on parts of military vessels (frigates), made the classifier confuse the modified frigates with civilian vessels. We termed such patterns adversarial camouflage (AC) since these patterns effectively the frigates with respect to the classifier. The type of attack described in our previous work is a so-called white box attack. This term describes attacks that are devised given full knowledge of the classifier under attack. This is as opposed to black box attacks, which describe attacks on unknown classifiers. In our context, the ultimate goal is to design a GAN that is capable of black box attacks, in other words: a GAN that will generate AC that has effect across a wide range of neural network classifiers. In the current work, we study techniques to improve the robustness of our GAN-based approach by investigating whether a GAN can be trained to fool a selection of several neural network-based classifiers, or reduce the confidence of the classifications to a degree which makes them unreliable. Our results indicate that it is indeed possible to weaken a wider range of neural network classifiers by training the generator on several classifiers.

The goal of speech super-resolution (SSR) or speech bandwidth expansion is to generate the missing high-frequency components for a given low-resolution speech signal. It has the potential to improve the quality of telecommunications. We propose a new method for SSR that leverages the generative adversarial networks (GANs) and a regularization method for stabilizing the GAN training. The generator network is a convolutional autoencoder with 1D convolution kernels, operating along time-axis and generating the high-frequency log-power spectra from the low-frequency log-power spectra input. We employ two recent deep neural network (DNN) based approaches to compare them with our proposed method, including both objective speech quality metrics and subjective perceptual tests. We show that our proposed method outperforms the baseline methods in terms of both objective and subjective evaluations.

SummaryThe explosive growth of various computer vision technologies generates a tremendous amount of visual data online every day. In addition to bringing convenience and revolutionizing our daily life, image data also reveal a wide range of sensitive information and pose unprecedented privacy leakage risks. Particularly, in the case of photos contain human faces, people can easily access those face images on social media without any consent, and the misuse of personal information could cause serious privacy violation to individuals. Therefore, it is essential to consider sanitizing people's identity information when using images containing human faces. As a result, there has been rapid development in the area of facial anonymization, also called image de‐identification. However, due to the emergence of numerous deep‐learning based attacks, traditional anonymization methods such as blurring and mosaic are weak and ineffective to protect individual's privacy in face images. To respond to this challenge, this article proposes a novel de‐identification method that utilizes a deep neural network. The proposed framework encompasses two modules: encoder network and generator network. The encoder transforms a face image into a high‐semantic latent vector of codes, which will be de‐identified according to the differential privacy criterion. The generator leverages the unconditional generative adversarial network to synthesize high‐quality images based on the modified latent codes from the encoder. Extensive experimental results indicate that our proposed model can protect image privacy while keeping the processed image visual realistic.

Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.

The purpose of the article is in finding out the level of influence of Artificial Intelligence on the graphic design on the example of operationalization of neural networks, which made generative graphics possible, allowed for a high variability of parameters, as well as adjusting the color scheme, contrast, and geometric proportions of the objects being created. Research methods. The empirical method, the method of analysis and synthesis, as well as qualitative research methods were used, which, in particular, include the study of scientific, popular and web sources on the use of generative networks and AI in graphic design; the features and functional parameters of individual neural networks in the context of the topic of the article were analyzed. In addition, along with a review of the latest research on the topic, the relationship between experimental and scientific research in graphic design today and the development of generative design as a promising technology with a wide range of applications and a method for designing functional structures of graphic design, UI/UX design, animation, etc. is clarified. Scientific novelty. The article presents for the first time a comprehensive approach to the problem of applying AI and generative neural networks in graphic design, and considers, in particular, the potential, advantages, and disadvantages of specific neural networks, such as DALL-E, DALL-E 2, Stable Diffusion, MidJourney, and Craiyon, when generating compositional and graphic solutions. Conclusions. It was found out that generative neural networks are one of the most popular types of AI in the design industry in the first quarter of the twenty-first century. Neural networks such as DALL-E, DALL-E 2, Stable Diffusion, MidJourney, and Craiyon, or professional programs such as InDesign or Illustrator, have definitely become indispensable for many graphic designers today, who use them to reproduce, transform, and synthesize images, manipulate elements according to the needs of users, improve and edit variations of generated images, as well as adjust geometric proportions, color and contrast of objects, and use a high variability of parameters.