Stable and Refined Style Transfer Using Zigzag Learning Algorithm

Style transfer is an optimization technique used to create a new image out of a content image and a style image (as in an artistic work by a well-known painter) as a result of blending, the content image is altered to be painted in the same style as the content image but to resemble it of the style image. The essential method for creating style transfer is the convolutional neural network that allows for transfer (CNN). This paper will analyse key methods for performing style transfer photos and quickly compare multiple models and their outcomes. We primarily contrast one pioneering technique, developed by Leon Gatys in 2015, with the most recent technology in the High-Resolution Network from 2019 for the transfer of photorealistic style. Gatys's Neural Algorithm of Artistic Style produces excellent results, but the resulting data doesn't maintain the content image's characteristics and the fact that the paper gives no concise description of the internal mechanisms gram matrix. The enhancement is the conversion to a photorealistic style transfer from Gatys' neural style transfer. It aids in maintaining the structure and defining characteristics within the content image. Both methods make use of VGG, a network that was trained using the ImageNet dataset for conducting image categorization. We made our own data set with five types or groups and used transfer learning in a VGG network to do a style transfer using transfer learning.

The development of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) has catalysed the emergence of the Neural Style Transfer (NST) field. NST elucidates the computer's comprehension of artistic elements, holding paramount significance in both academic and practical domains. In this paper, we introduce a novel system termed "Content-Adaptive Style Transfer." This system incorporates two additional techniques: object segmentation and image fusion, augmenting the customizability of NST. Our experimental results underscore its capability to execute style transfer exclusively on the foreground, leaving the background unaffected, or to modify the background style while preserving the original foreground. These innovations promise more rational outcomes in specific scenarios.

The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer algorithms can apply the artistic style of one image to another image without changing the latter's high-level semantic content, which makes it feasible to employ neural style transfer as a data augmentation method to add more variation to the training dataset. The contribution of this paper is a thorough evaluation of the effectiveness of the neural style transfer as a data augmentation method for image classification tasks. We explore the state-of-the-art neural style transfer algorithms and apply them as a data augmentation method on Caltech 101 and Caltech 256 dataset, where we found around 2% improvement from 83% to 85% of the image classification accuracy with VGG16, compared with traditional data augmentation strategies. We also combine this new method with conventional data augmentation approaches to further improve the performance of image classification. This work shows the potential of neural style transfer in computer vision field, such as helping us to reduce the difficulty of collecting sufficient labelled data and improve the performance of generic image-based deep learning algorithms.

Stylized image denoising via noise style transfer and Quasi Siamese network

Art in general and fine arts, in particular, play a significant role in human life, entertaining and dispelling stress and motivating their creativeness in specific ways. Many well-known artists have left a rich treasure of paintings for humanity, preserving their exquisite talent and creativity through unique artistic styles. In recent years, a technique called ’style transfer’ allows computers to apply famous artistic styles into the style of a picture or photograph while retaining the shape of the image, creating superior visual experiences. The basic model of that process, named ’Neural Style Transfer,’ has been introduced promisingly by Leon A. Gatys; however, it contains several limitations on output quality and implementation time, making it challenging to apply in practice. Based on that basic model, an image transform network was proposed in this paper to generate higher-quality artwork and higher abilities to perform on a larger image amount. The proposed model significantly shortened the execution time and can be implemented in a real-time application, providing promising results and performance. The outcomes are auspicious and can be used as a referenced model in color grading or semantic image segmentation, and future research focuses on improving its applications.

Artistic style transfer is one the enormous research fields in computer vision. It is a technique to synthesize a content image and the style together to form an stylized image. Various popular applications like Prisma, Deepart, Pikazoapp provide a platform to synthesis images in order to produce very exciting artistic style. These applications use Convolutional Neural Networks to transform the images in an artistic style. Prisma photo editor has more than 250 modern art filters and 110 million users. Hence, the amount of images being synthesized is huge and computation requires to perform the task efficiently is large. Abundant CNN architectures like VGG16, VGG19, GoogleNet etc are introduced to solve this problem of heavy computations. The system helps users to transform their images into artworks using style of famous artists for stirring image filters, and it might be useful for artists to have an indication about the art. This paper presents the fundamental concepts, classification of Style Transfer and major progress towards Neural Style Transfer.

Neural style transfer represented a paradigm shift in image artistic rendering and applications in digital entertainment. Generative adversarial networks are considered as a general solution for style transfer problems. Researchers have explored multiple learning algorithms to increase the learning ability of style transfer networks. However, such research has overlooked an important outside motivator: social preference. Classical style transfer networks are trained offline and worked as a stationary model to transfer photos into stylized images, without any interaction with the environment. Based on the ideas from online training, we propose a new coarse-to-fine training strategy for neural style transfer systems to adapt to social preference change. In coarse stage, a primary model is learned via normal training method. In fine stage, the model is updated with online learning approach and sequentially added new data. We show that our approach exhibits improved performance compared to stationary model from visual effect and reflection of social preference. We conclude that the coarse-to-fine training strategy can improve the output of the generative model in social media environment.

ReLU-oscillator: Chaotic VGG10 model for real-time neural style transfer on painting authentication

Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features often focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at different depths, the resulting images are often unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as different objects becomes obscured. We observe that the depth map effectively reflects the spatial distribution in an image and preserving the depth map of the content image after stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer.

Portrait style transfer using deep convolutional neural networks and facial segmentation

Artistic Style Transfer(AST) a fascinating aspect of Neural Style Transfer(NST) has redefined the creative manipulation of digital images by imbuing photographs with the aesthetic characteristics of famous paintings. However a persistent challenge in NST-color distortion—often compromises the color fidelity of the content image during the style transfer process. This paper introduces an innovative approach employing the VGG-19 neural network to extract style features while preserving the color harmony of the content image through luminance transfer. This method ensures that the original color palette remains unaltered achieving an authentic blend of style and content characteristics. In our experimentation, we applied the innovative artistic style transfer technique to a variety of content images, incorporating the aesthetic elements from renowned paintings. Notably, our method successfully preserved the color fidelity of the content images throughout the style transfer process. This approach contributes significantly to the field by offering a improved technique for NST that harmonizes artistic style with natural coloration serving a valuable asset for artists and technologists.

Enhancing the detection of atrial fibrillation from wearable sensors with neural style transfer and convolutional recurrent networks

The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning with Neural style transfer that has been a high risk application for deep learning, make attention from and advertising the effectiveness to both the academic prisons and the general public. However, we have found by removal experiments that optimizing an image in the way neural style transfer does, we can even factor out the deepness (multiple layers of exchange linear and nonlinear transformations) all together and have neural style transfer working to a certain range. We present the VGG-19 Artistic Type Neural Algorithm, which can convert and recombine the image quality and style of natural images. This algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous well known portrait. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks (CNN) and demonstrate their potential for high level image synthesis and manipulation.

At present, neural style transfer technique is gaining more and more popularity in different fields like sports and entertainment. During the artistic style transfer of an image, style loss and content loss occurs. Several research works are being performed in present with a view to reduce the rate of both style and content loss, but some of these processes require much time for style transfer. In this paper, we propose a style transfer method using convolutional neural network that minimizes the rate of style and content loss in a minimal time compared to some other works. The proposed method is applied on different images and artworks. The results are compared with some recent research works related to this method and the proposed method is found to be 5–7% more efficient and faster than those related works.

The neural style transfer has achieved a significant improvement with deep learning methods. However, the existing methods are susceptible to lack the ability for handling the texture style transfer because of their less consideration of the textural structure from style images. To overcome this drawback, this letter presents a simple method to capture the textural structure by using an adaptive auto-correlation alignment loss function. Furthermore, we also introduce three metrics to quantitatively evaluate the performance. We qualitatively and quantitatively evaluate the proposed methods. The experimental results demonstrate the superiority of the proposed method and our method can synthesize the stylized images with rich texture style patterns.