Offline Handwritten Signature Authentication with Conditional Deep Convolutional Generative Adversarial Networks

2021 ◽  
Author(s):  
David C. Yonekura ◽  
Elloá B. Guedes
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Generative Adversarial Networks ◽  
Equal Error Rate ◽  
Training Time ◽  
Adversarial Networks ◽  
Handwritten Signature ◽  
State Of Art

Handwritten signature authentication systems are important in many real world scenarios to avoid frauds. Thanks to Deep Learning, state-of-art solutions have been proposed to this problem by making use of Convolutional Neural Networks, but other models in this Machine Learning subarea are still to be further explored. In this perspective, the present article introduces a Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) approach whose experimental results in a realistic dataset with skilled forgeries have Equal Error Rate (EER) of 18.53% and balanced accuracy of 87.91%. These results validate a writerdependent cDCGAN-based solution to the signature authentication problem in a real world scenario where no forgeries are available nor required in training time.

Deep Learning and Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 562-575
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

scholarly journals Deep learning in bioinformatics: introduction, application, and perspective in big data era

2019 ◽  
Author(s):  
Yu Li ◽  
Chao Huang ◽  
Lizhong Ding ◽  
Zhongxiao Li ◽  
Yijie Pan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Deep Learning ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Research Directions ◽  
Adversarial Networks ◽  
Variational Autoencoder ◽  
The Common ◽  
Graph Neural Networks

AbstractDeep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.

scholarly journals LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

2020 ◽  
Vol 10 (14) ◽  
pp. 4913
Author(s):  
Tin Kramberger ◽  
Božidar Potočnik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Dataset ◽  
Image Datasets

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

scholarly journals Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 130 ◽  
Author(s):  
Mohammad Navid Fekri ◽  
Ananda Mohon Ghosh ◽  
Katarina Grolinger
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Energy Consumption ◽  
Smart Grid ◽  
Time Series Data ◽  
Security And Privacy ◽  
Series Data ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data, Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models.

scholarly journals Application of Rotating Machinery Fault Diagnosis Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-30
Author(s):  
Wei Cui ◽  
Guoying Meng ◽  
Aiming Wang ◽  
Xinge Zhang ◽  
Jun Ding
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Normal Operation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Safe Production ◽  
Continuous Progress ◽  
Machinery Fault Diagnosis

With the continuous progress of modern industry, rotating machinery is gradually developing toward complexity and intelligence. The fault diagnosis technology of rotating machinery is one of the key means to ensure the normal operation of equipment and safe production, which has very important significance. Deep learning is a useful tool for analyzing and processing big data, which has been widely used in various fields. After a brief review of early fault diagnosis methods, this paper focuses on the method models that are widely used in deep learning: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and transfer learning methods are summarized from the two aspects of principle and application in the field of fault diagnosis of rotating machinery. Then, the commonly used evaluation indicators used to evaluate the performance of rotating machinery fault diagnosis methods are summarized. Finally, according to the current research status in the field of rotating machinery fault diagnosis, the current problems and possible future development and research trends are discussed.

scholarly journals Object-Oriented Generative Adversarial Networks

2020 ◽  
Author(s):  
Qianli Liao
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Object Recognition ◽  
Object Oriented ◽  
Generative Adversarial Networks ◽  
Statistical Features ◽  
Connectionist Models ◽  
Adversarial Networks ◽  
Class Project ◽  
High Level

(Performed in 2018 as a class project) Deep learning is a field that has been mainly driven by connectionist models like neural networks, characterized by layered processing of distributed, sub-symbolic and statistical features. However, human high-level thoughts appear to be highly symbolic, focusing on objects and relations.To bridge the gap between perception and symbols, a series of models on "Object Oriented Deep Learning" was proposed [9,8,7]. In this project we further explore this class of models. We implement a generative version of OODL that can generate images instead of performing object recognition, in a similar way to Generative Adversarial Networks (GANs). In comparison to conventional “feature-oriented” deep learning, OODL naturally handles properties of objects by incorporating them as fields. It offers exact equivariance [8] to translation, rotation and scaling. When implementing it as a generative model, one should be able to precisely control such geometric properties of the generated objects.

scholarly journals From Reflex to Reflection: Two Tricks AI Could Learn from Us

Philosophies ◽  
2019 ◽  
Vol 4 (2) ◽  
pp. 27
Author(s):  
Jean-Louis Dessalles
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Processes ◽  
Real World ◽  
Machine Learning Techniques ◽  
Human Intervention ◽  
Real World Data ◽  
World Data ◽  
Learning Techniques

Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection.

Deep Learning and Biomedical Engineering

2018 ◽  
pp. 283-296
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

scholarly journals Wasserstein Generative Adversarial Networks Based Data Augmentation for Radar Data Analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 1449
Author(s):  
Hansoo Lee ◽  
Jonggeun Kim ◽  
Eun Kyeong Kim ◽  
Sungshin Kim
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Weather Radar ◽  
Image Synthesis ◽  
Radar Data ◽  
Generative Adversarial Networks ◽  
Necessary Condition ◽  
Radar Images ◽  
Adversarial Networks ◽  
Wide Range

Ground-based weather radar can observe a wide range with a high spatial and temporal resolution. They are beneficial to meteorological research and services by providing valuable information. Recent weather radar data related research has focused on applying machine learning and deep learning to solve complicated problems. It is a well-known fact that an adequate amount of data is a positively necessary condition in machine learning and deep learning. Generative adversarial networks (GANs) have received extensive attention for their remarkable data generation capacity, with a fascinating competitive structure having been proposed since. Consequently, a massive number of variants have been proposed; which model is adequate to solve the given problem is an inevitable concern. In this paper, we propose exploring the problem of radar image synthesis and evaluating different GANs with authentic radar observation results. The experimental results showed that the improved Wasserstein GAN is more capable of generating similar radar images while achieving higher structural similarity results.

scholarly journals Color-Patterns to Architecture Conversion through Conditional Generative Adversarial Networks

Biomimetics ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Diego Navarro-Mateu ◽  
Oriol Carrasco ◽  
Pedro Cortes Cortes Nieves
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Color Pattern ◽  
Generative Adversarial Networks ◽  
Architecture Design ◽  
Learning Tools ◽  
Color Patterns ◽  
Adversarial Networks ◽  
Recent Developments ◽  
Evo Devo

Often an apparent complex reality can be extrapolated into certain patterns that in turn are evidenced in natural behaviors (whether biological, chemical or physical). The Architecture Design field has manifested these patterns as a conscious (inspired designs) or unconscious manner (emerging organizations). If such patterns exist and can be recognized, can we therefore use them as genotypic DNA? Can we be capable of generating a phenotypic architecture that is manifestly more complex than the original pattern? Recent developments in the field of Evo-Devo around gene regulators patterns or the explosive development of Machine Learning tools could be combined to set the basis for developing new, disruptive workflows for both design and analysis. This study will test the feasibility of using conditional Generative Adversarial Networks (cGANs) as a tool for coding architecture into color pattern-based images and translating them into 2D architectural representations. A series of scaled tests are performed to check the feasibility of the hypothesis. A second test assesses the flexibility of the trained neural networks against cases outside the database.

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