Neural Style Transfer Using VGG19 and Alexnet

Kavitha, S.; Dhanapriya, B.; Vignesh, G. Naveen; Baskaran, K. R.

doi:10.1109/icaeca52838.2021.9675723

Cited by 15 publications

(7 citation statements)

References 2 publications

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“…Note that while the CycleGAN does not require a corresponding style image to generate stylized images as it directly learns transferable representations from the content image, we observed that the CycleGAN generated suboptimal results for our problem compared to the neural style transfer algorithm. For our study, we used a combination of two different approaches for synthetic image generation with the neural style transfer algorithm: A VGG-19 model architecture (Simonyan and Zisserman, 2014; Kavitha et al, 2021), originally pretrained with weights from ImageNet for image classification. A pretrained fast style transfer model leveraging arbitrary image stylization (Ghiasi et al, 2017) (no fine-tuning required). …”

Section: Proposed Methodology and Learning Modelsmentioning

confidence: 99%

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Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Chatterjee

Nieto

Gelbhardt

et al. 2023

Environ. Data Science

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Wind energy’s ability to liberate the world from conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies have utilized deep learning (DL) techniques to predict icing events with high accuracy by leveraging rotor blade images, but these studies only focus on specific wind parks and fail to generalize to unseen scenarios (e.g., new rotor blade designs). In this paper, we aim to facilitate ice prediction on the face of lack of ice images in new wind parks. We propose the utilization of synthetic data augmentation via a generative artificial intelligence technique—the neural style transfer algorithm to improve the generalization of existing ice prediction models. We also compare the proposed technique with the CycleGAN as a baseline. We show that training standalone DL models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable toward tackling climate change.

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Section: Proposed Methodology and Learning Modelsmentioning

confidence: 99%

“…1. A VGG-19 model architecture (Simonyan and Zisserman, 2014;Kavitha et al, 2021), originally pretrained with weights from ImageNet for image classification. 2.…”

Section: Neural Style Transfer Algorithmmentioning

confidence: 99%

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Chatterjee

Nieto

Gelbhardt

et al. 2023

Environ. Data Science

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“…As its name suggests, VGG19 includes 19 layers. Instead of using large filters, the VGG19 model uses multiple 3 × 3 filters per layer, with a size of 11 × 11 like Alexnet [32,33].…”

Section: Proposed Ensemble Modelmentioning

confidence: 99%

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

T黵k¹

2023

Computer Systems Science and Engineering

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Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the negative results caused by the virus and the treatment costs, the importance of fast and accurate diagnosis is clearly seen. In this context, deep learning methods appear as an extremely popular approach. In this study, a hybrid model design with superior properties of convolutional neural networks is presented to correctly classify the Covid-19 disease. In addition, in order to contribute to the literature, a suitable dataset with balanced case numbers that can be used in all artificial intelligence classification studies is presented. With this ensemble model design, quite remarkable results are obtained for the diagnosis of three and four-class Covid-19. The proposed model can classify normal, pneumonia, and Covid-19 with 92.6% accuracy and 82.6% for normal, pneumonia, Covid-19, and lung opacity.

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“…Style transfer is a program that can automatically extract style features in an image and transfer these features to a different image and make this image with a new style look normal [1]. The main content of the style transfer is the convolution neural network, which can extract the eigenvectors in the image layer by layer via the convolution kernel in the hidden layer [2]. A well-trained style transfer model could transfer the fitted functions in the training to the new pixel matrix through a multilinear operation.…”

Section: Introductionmentioning

confidence: 99%

“…Current style transfer methods maintain the structure information and main content information and convert the information of colors, lines, light, etc, which only serves as a filter instead of recreating the image. This research aims to reduce the randomness in style transfer by converting the image preprocessing methods and multi-applying the VGG network which is suitable for image processing [2]. In the research, we deconstructed the concept of style by digitizing information in the artistic field and extracting structured information from the concept to extract the key elements of style through the linear operation.…”

Section: Introductionmentioning

confidence: 99%

Style transfer with VGG19

Lang

2023

ACE

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Style transfer is a wide-used technique in image and photograph processing, which could transfer the style of an image to a target image that has a different content. This image processing technique has been used in the algorithms of some image processing software as well as modern artistic creation. However, the intrinsic principle of style transfer and its transfer accuracy is still not clear and stable. This article discusses a new method for preprocessing image data that uses feature extraction and forming vector fields and utilizing multiple VGG19 to separately train the distinct features in images to obtain a better effect in predicting. Our model could generate more autonomous and original images that are not simply adding a style filter to the image, which can help the development of AI style transfer and painting.

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Neural Style Transfer Using VGG19 and Alexnet

Cited by 15 publications

References 2 publications

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Domain-invariant icing detection on wind turbine rotor blades with generative artificial intelligence for deep transfer learning

Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

Style transfer with VGG19

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