Digital Transformation

2020 ◽  
pp. 1-18
Author(s):  
Henri Schildt
Keyword(s):  
Language Processing ◽  
Digital Transformation ◽  
Digital Data ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Geographic Boundaries ◽  
Technological Advances ◽  
Data Flows ◽  
Speed Up ◽  
Systematic Shift

The introductory chapter to the book The Data Imperative examines how technological advances together with a new managerial mindset are driving digital transformation. While early business information systems were often self-contained and designed to solve specific problems, contemporary systems are highly interconnected and integrated. Corporations can use data flows to coordinate diverse processes and activities across organizational and geographic boundaries. The chapter explains how digital transformation involves a systematic shift from predominant reliance on human knowledge and skills to digital data flows and smart algorithms. Artificial intelligence techniques, such as generative adversarial networks and advanced natural language processing, and 5G wireless technologies create new opportunities to replace human routines with algorithmic processing. Data will continue to break down organizational silos, enable deeper collaboration across company boundaries, and speed up the development of new services.

scholarly journals Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs

2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
David Vint ◽  
Matthew Anderson ◽  
Yuhao Yang ◽  
Christos Ilioudis ◽  
Gaetano Di Caterina ◽  
...  
Keyword(s):  
Target Recognition ◽  
Automatic Target Recognition ◽  
Generative Adversarial Networks ◽  
Low Resolution ◽  
Sar Images ◽  
Adversarial Networks ◽  
Technological Advances ◽  
Dataset Size ◽  
Resolution Imaging ◽  
High Level

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.

scholarly journals Reconstruction of Generative Adversarial Networks in Cross Modal Image Generation with Canonical Polyadic Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ruixin Ma ◽  
Junying Lou ◽  
Peng Li ◽  
Jing Gao
Keyword(s):  
Mobile Terminal ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Adversarial Networks ◽  
Speed Up ◽  
Canonical Polyadic Decomposition

Generating pictures from text is an interesting, classic, and challenging task. Benefited from the development of generative adversarial networks (GAN), the generation quality of this task has been greatly improved. Many excellent cross modal GAN models have been put forward. These models add extensive layers and constraints to get impressive generation pictures. However, complexity and computation of existing cross modal GANs are too high to be deployed in mobile terminal. To solve this problem, this paper designs a compact cross modal GAN based on canonical polyadic decomposition. We replace an original convolution layer with three small convolution layers and use an autoencoder to stabilize and speed up training. The experimental results show that our model achieves 20% times of compression in both parameters and FLOPs without loss of quality on generated images.

scholarly journals One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks

2019 ◽  
Author(s):  
Tao He ◽  
Yuan-Fang Li ◽  
Lianli Gao ◽  
Dongxiang Zhang ◽  
Jingkuan Song
Keyword(s):  
Adaptive Learning ◽  
Digital Data ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Source Domain ◽  
Learning Framework ◽  
Common Space ◽  
Adversarial Networks ◽  
Shared Space ◽  
Benchmark Datasets

With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains’ images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on four public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.

scholarly journals Potential role of artificial intelligence in craniofacial surgery

2021 ◽  
Vol 22 (5) ◽  
pp. 223-231
Author(s):  
Jeong Yeop Ryu ◽  
Ho Yun Chung ◽  
Kang Young Choi
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Language Processing ◽  
Cosmetic Surgery ◽  
Craniofacial Surgery ◽  
Generative Adversarial Networks ◽  
Biosignal Analysis ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Craniofacial Trauma

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.

Generative Adversarial Networks

2021 ◽  
Vol 54 (6) ◽  
pp. 1-38
Author(s):  
Zhipeng Cai ◽  
Zuobin Xiong ◽  
Honghui Xu ◽  
Peng Wang ◽  
Wei Li ◽  
...  
Keyword(s):  
Language Processing ◽  
Generative Adversarial Networks ◽  
Future Research ◽  
Data Generation ◽  
Optimization Strategy ◽  
Privacy And Security ◽  
Adversarial Networks ◽  
Initial Stage ◽  
Game Theoretic ◽  
Future Research Directions

Generative Adversarial Networks (GANs) have promoted a variety of applications in computer vision and natural language processing, among others, due to its generative model’s compelling ability to generate realistic examples plausibly drawn from an existing distribution of samples. GAN not only provides impressive performance on data generation-based tasks but also stimulates fertilization for privacy and security oriented research because of its game theoretic optimization strategy. Unfortunately, there are no comprehensive surveys on GAN in privacy and security, which motivates this survey to summarize systematically. The existing works are classified into proper categories based on privacy and security functions, and this survey conducts a comprehensive analysis of their advantages and drawbacks. Considering that GAN in privacy and security is still at a very initial stage and has imposed unique challenges that are yet to be well addressed, this article also sheds light on some potential privacy and security applications with GAN and elaborates on some future research directions.

scholarly journals OptAGAN: Entropy-based Finetuning on Text VAE-GAN

2021 ◽  
Author(s):  
Paolo Tirotta ◽  
Stefano Lodi
Keyword(s):  
Language Processing ◽  
Generative Adversarial Networks ◽  
Word Generation ◽  
Adversarial Networks ◽  
Current State ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Maximum Likelihood Methods ◽  
High Level

Transfer learning through large pre-trained models has changed the landscape of current applications in natural language processing (NLP). Recently Optimus, a variational autoencoder (VAE) which combines two pre-trained models, BERT and GPT-2, has been released, and its combination with generative adversarial networks (GANs) has been shown to produce novel, yet very human-looking text. The Optimus and GANs combination avoids the troublesome application of GANs to the discrete domain of text, and prevents the exposure bias of standard maximum likelihood methods. We combine the training of GANs in the latent space, with the finetuning of the decoder of Optimus for single word generation. This approach lets us model both the high-level features of the sentences, and the low-level word-by-word generation. We finetune using reinforcement learning (RL) by exploiting the structure of GPT-2 and by adding entropy-based intrinsically motivated rewards to balance between quality and diversity. We benchmark the results of the VAE-GAN model, and show the improvements brought by our RL finetuning on three widely used datasets for text generation, with results that greatly surpass the current state-of-the-art for the quality of the generated texts.

scholarly journals Image Synthesis Based On Feature Description

2021 ◽  
Vol 9 (8) ◽  
pp. 2492-2494
Author(s):  
Rohan Bolusani
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Language Processing ◽  
Image Synthesis ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Feature Representations ◽  
Adversarial Networks ◽  
Text Feature ◽  
Image Modelling

Abstract: Generating realistic images from text is innovative and interesting, but modern-day machine learning models are still far from this goal. With research and development in the field of natural language processing, neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, in the field of machine learning, generative adversarial networks (GANs) have begun to generate extremely accurate images of especially in categories, such as faces, album covers, and room interiors. In this work, the main goal is to develop a neural network to bridge these advances in text and image modelling, by essentially translating characters to pixels the project will demonstrate the capability of generative models by taking detailed text descriptions and generate plausible images. Keywords: Deep Learning, Computer Vision, NLP, Generative Adversarial Networks

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