scholarly journals Icon Generation Based on Generative Adversarial Networks

2021 ◽  
Vol 11 (17) ◽  
pp. 7890
Author(s):  
Hongyi Yang ◽  
Chengqi Xue ◽  
Xiaoying Yang ◽  
Han Yang
Keyword(s):  
User Study ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Learning Technology ◽  
Ui Design ◽  
Training Techniques ◽  
Adversarial Networks ◽  
Art Research ◽  
Spectral Normalization ◽  
Degree Of Innovation

Icon design is an important part of UI design, and a design task that designers often encounter. During the design process, it is important to highlight the function of icons themselves and avoid excessive similarity with similar icons, i.e., to have a certain degree of innovation and uniqueness. With the rapid development of deep learning technology, generative adversarial networks (GANs) can be used to assist designers in designing and updating icons. In this paper, we construct an icon dataset consisting of 8 icon categories, and introduce state-of-the-art research and training techniques including attention mechanism and spectral normalization based on the original StyleGAN. The results show that our model can effectively generate high-quality icons. In addition, based on the user study, we demonstrate that our generated icons can be useful to designers as design aids. Finally, we discuss the potential impacts and consider the prospects for future related research.

scholarly journals WGAN-E: A Generative Adversarial Networks for Facial Feature Security

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 486
Author(s):  
Chunxue Wu ◽  
Bobo Ju ◽  
Yan Wu ◽  
Neal N. Xiong ◽  
Sheng Zhang
Keyword(s):  
Face Recognition ◽  
Intelligent Systems ◽  
Human Life ◽  
Facial Feature ◽  
Generative Adversarial Networks ◽  
Facial Features ◽  
Learning Technology ◽  
Adversarial Networks ◽  
Face Features ◽  
Hashing Algorithm

Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality.

scholarly journals Lipschitz constrained GANs via boundedness and continuity

2020 ◽  
Vol 32 (24) ◽  
pp. 18271-18283
Author(s):  
Kanglin Liu ◽  
Guoping Qiu
Keyword(s):  
Neural Network ◽  
Computational Complexity ◽  
Convolutional Neural Network ◽  
Generative Adversarial Networks ◽  
Heuristic Methods ◽  
Performance Control ◽  
Effective Implementation ◽  
Adversarial Networks ◽  
Spectral Normalization

AbstractOne of the challenges in the study of generative adversarial networks (GANs) is the difficulty of its performance control. Lipschitz constraint is essential in guaranteeing training stability for GANs. Although heuristic methods such as weight clipping, gradient penalty and spectral normalization have been proposed to enforce Lipschitz constraint, it is still difficult to achieve a solution that is both practically effective and theoretically provably satisfying a Lipschitz constraint. In this paper, we introduce the boundedness and continuity (BC) conditions to enforce the Lipschitz constraint on the discriminator functions of GANs. We prove theoretically that GANs with discriminators meeting the BC conditions satisfy the Lipschitz constraint. We present a practically very effective implementation of a GAN based on a convolutional neural network (CNN) by forcing the CNN to satisfy the BC conditions (BC–GAN). We show that as compared to recent techniques including gradient penalty and spectral normalization, BC–GANs have not only better performances but also lower computational complexity.

scholarly journals GENERATING SYNTHETIC TRAINING DATA FOR OBJECT DETECTION USING MULTI-TASK GENERATIVE ADVERSARIAL NETWORKS

2020 ◽  
Vol V-2-2020 ◽  
pp. 443-449
Author(s):  
Y. Lin ◽  
K. Suzuki ◽  
H. Takeda ◽  
K. Nakamura
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rapid Development ◽  
Real Data ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Great Success ◽  
Traffic Signs ◽  
Adversarial Networks ◽  
The Neural Network

Abstract. Nowadays, digitizing roadside objects, for instance traffic signs, is a necessary step for generating High Definition Maps (HD Map) which remains as an open challenge. Rapid development of deep learning technology using Convolutional Neural Networks (CNN) has achieved great success in computer vision field in recent years. However, performance of most deep learning algorithms highly depends on the quality of training data. Collecting the desired training dataset is a difficult task, especially for roadside objects due to their imbalanced numbers along roadside. Although, training the neural network using synthetic data have been proposed. The distribution gap between synthetic and real data still exists and could aggravate the performance. We propose to transfer the style between synthetic and real data using Multi-Task Generative Adversarial Networks (SYN-MTGAN) before training the neural network which conducts the detection of roadside objects. Experiments focusing on traffic signs show that our proposed method can reach mAP of 0.77 and is able to improve detection performance for objects whose training samples are difficult to collect.

scholarly journals Multimodal Storytelling via Generative Adversarial Imitation Learning

2017 ◽  
Author(s):  
Zhiqian Chen ◽  
Xuchao Zhang ◽  
Arnold P. Boedihardjo ◽  
Jing Dai ◽  
Chang-Tien Lu
Keyword(s):  
User Study ◽  
Information Overload ◽  
Imitation Learning ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Proposed Model ◽  
Unseen Data ◽  
Extensive Information ◽  
Definition Of ◽  
Critical Challenge

Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized definition of storyline metric. Prior studies have developed various approaches based on different assumptions about users' interests. These works can extract interesting patterns, but their assumptions do not guarantee that the derived patterns will match users' preference. On the other hand, their exclusiveness of single modality source misses cross-modality information. This paper proposes a method, multimodal imitation learning via Generative Adversarial Networks(MIL-GAN), to directly model users' interests as reflected by various data. In particular, the proposed model addresses the critical challenge by imitating users' demonstrated storylines. Our proposed model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. The proposed approach is demonstrated to be capable of acquiring the user's implicit intent and outperforming competing methods by a substantial margin with a user study.

scholarly journals Data Augmentation using Adversarial Networks for Tea Diseases Detection

2020 ◽  
Vol 20 (1) ◽  
pp. 29
Author(s):  
R. Sandra Yuwana ◽  
Fani Fauziah ◽  
Ana Heryana ◽  
Dikdik Krisnandi ◽  
R. Budiarianto Suryo Kusumo ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Original Data ◽  
The Other ◽  
Generative Adversarial Networks ◽  
Learning Technology ◽  
Limited Data ◽  
Adversarial Networks

Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming.  On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented.  By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception.  The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%. 

Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning

2021 ◽  
Vol 11 (3-4) ◽  
pp. 1-34
Author(s):  
Chris Kim ◽  
Xiao Lin ◽  
Christopher Collins ◽  
Graham W. Taylor ◽  
Mohamed R. Amer
Keyword(s):  
Machine Learning ◽  
Motion Capture ◽  
User Study ◽  
Generative Adversarial Networks ◽  
Discriminative Models ◽  
Adversarial Networks ◽  
Machine Learning Approach ◽  
Text Query ◽  
High Level

While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.

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