scholarly journals Digital Image Art Style Transfer Algorithm Based on CycleGAN

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Xuhui Fu
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Image Size ◽  
Image Detection ◽  
Style Transfer ◽  
Artistic Style ◽  
Multi Scale ◽  
The Core ◽  
Artificial Intelligence Technology ◽  
Art Style

With the continuous development and popularization of artificial intelligence technology in recent years, the field of deep learning has also developed relatively rapidly. The application of deep learning technology has attracted attention in image detection, image recognition, image recoloring, and image artistic style transfer. Some image art style transfer techniques with deep learning as the core are also widely used. This article intends to create an image art style transfer algorithm to quickly realize the image art style transfer based on the generation of confrontation network. The principle of generating a confrontation network is mainly to change the traditional deconvolution operation, by adjusting the image size and then convolving, using the content encoder and style encoder to encode the content and style of the selected image, and by extracting the content and style features. In order to enhance the effect of image artistic style transfer, the image is recognized by using a multi-scale discriminator. The experimental results show that this algorithm is effective and has great application and promotion value.

scholarly journals Design of College Evaluation System Based on Artificial Intelligence and Deep Learning Technology

CONVERTER ◽  
2021 ◽  
pp. 651-658
Author(s):  
Jiang Yan, Wang Peipei
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Performance Appraisal ◽  
Evaluation System ◽  
Learning Technology ◽  
Functional Requirements ◽  
Appraisal System ◽  
Performance Appraisal System ◽  
Artificial Intelligence Technology ◽  
Business Requirements

Artificial intelligence and deep learning technology are important technologies widely used in manufacturing industry.With the help of performance appraisal system to comprehensively evaluate the performance of teachers is a good measure. Therefore, it is very necessary to develop a performance appraisal system for university teachers by using artificial intelligence technology. This paper first demonstrates the feasibility of the development of performance appraisal system, and scientifically divides the user roles. According to the business requirements, the core business process of the system is established, and the system architecture and functional modules are designed. At the same time, this paper establishes the conceptual model and logical model of database. Finally, SSH framework and extjs framework are used to realize the functions of the system. In this paper, the reliability, stability and security of the system are tested to ensure that the system meets the functional and non functional requirements. The operation results show that the system has stable functions, simple operation and convenient maintenance, and basically meets the needs of users at different levels.

Comparing optimization methods for deep learning in image processing applications

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexander Geng ◽  
Ali Moghiseh ◽  
Claudia Redenbach ◽  
Katja Schladitz
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Optimization Methods ◽  
Optimization Method ◽  
Stochastic Gradient Descent ◽  
Style Transfer ◽  
Artistic Style ◽  
Deep Learning Network ◽  
The Individual ◽  
The Impact

Abstract Training a deep learning network requires choosing its weights such that the output minimizes a given loss function. In practice, stochastic gradient descent is frequently used for solving the optimization problem. Several variants of this approach have been suggested in the literature. We study the impact of the choice of the optimization method on the outcome of the learning process at the example of two image processing applications from quite different fields. The first one is artistic style transfer, where the content of one image is combined with the style of another one. The second application is a real world classification task from industry, namely detecting defects in images of air filters. In both cases, clear differences between the results of the individual optimization methods are observed.

scholarly journals Using Principal Paths to Walk Through Music and Visual Art Style Spaces Induced by Convolutional Neural Networks

2021 ◽  
Author(s):  
E. Gardini ◽  
M. J. Ferrarotti ◽  
A. Cavalli ◽  
S. Decherchi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visual Art ◽  
Dimensional Space ◽  
Evolutionary Information ◽  
Evolutionary Analysis ◽  
Style Transfer ◽  
Artistic Style ◽  
Analysis Protocol

AbstractComputational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights.

scholarly journals FNet: A Two-Stream Model for Detecting Adversarial Attacks against 5G-Based Deep Learning Services

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guangquan Xu ◽  
Guofeng Feng ◽  
Litao Jiao ◽  
Meiqi Feng ◽  
Xi Zheng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Networks ◽  
Learning Technology ◽  
Stream Network ◽  
Fifth Generation ◽  
Decision Network ◽  
Artificial Intelligence Technology ◽  
Adversarial Examples ◽  
The Difference

With the extensive application of artificial intelligence technology in 5G and Beyond Fifth Generation (B5G) networks, it has become a common trend for artificial intelligence to integrate into modern communication networks. Deep learning is a subset of machine learning and has recently led to significant improvements in many fields. In particular, many 5G-based services use deep learning technology to provide better services. Although deep learning is powerful, it is still vulnerable when faced with 5G-based deep learning services. Because of the nonlinearity of deep learning algorithms, slight perturbation input by the attacker will result in big changes in the output. Although many researchers have proposed methods against adversarial attacks, these methods are not always effective against powerful attacks such as CW. In this paper, we propose a new two-stream network which includes RGB stream and spatial rich model (SRM) noise stream to discover the difference between adversarial examples and clean examples. The RGB stream uses raw data to capture subtle differences in adversarial samples. The SRM noise stream uses the SRM filters to get noise features. We regard the noise features as additional evidence for adversarial detection. Then, we adopt bilinear pooling to fuse the RGB features and the SRM features. Finally, the final features are input into the decision network to decide whether the image is adversarial or not. Experimental results show that our proposed method can accurately detect adversarial examples. Even with powerful attacks, we can still achieve a detection rate of 91.3%. Moreover, our method has good transferability to generalize to other adversaries.

scholarly journals REAL TIME OBJECT DETECTION USING YOLO ALGORITHM

2021 ◽  
Vol 10 (7) ◽  
pp. 61-67
Author(s):  
Bobburi Taralathasri ◽  
Dammati Vidya Sri ◽  
Gadidammalla Narendra Kumar ◽  
Annam Subbarao ◽  
Palli R Krishna Prasad
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Detection System ◽  
Image Data ◽  
Image Detection ◽  
The Core ◽  
Wide Range ◽  
Driverless Cars ◽  
Learning Technique

The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Feature Fusion from Multiple Paintings for Generalized Artistic Style Transfer

2019 ◽  
Author(s):  
Utsav Krishnan ◽  
Akshal Sharma ◽  
Pratik Chattopadhyay
Keyword(s):  
Feature Fusion ◽  
Style Transfer ◽  
Artistic Style

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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