Deep Neural Networks for Symbol Generation
“image generation deep neural networks” “conditional GANs” “symbol generation” “Chinese cultural identity symbols” “GAN training process”
Mathematical Problems in Engineering
Theory and Application of Swarm Intelligence and Machine Learning
Research Article | Open Access
Research on the Creation of Chinese National Cultural Identity Symbols Based on Visual Images
LianShuQing Yang 1 and Jiahui Li
Academic Editor: Lianhui Li
Received 25 Jul 2022
Revised 23 Aug 2022
Accepted 29 Aug 2022
Published 13 Sept 2022
Abstract
As a photographer who has traveled extensively throughout China, I understand the importance of creating cultural symbols that accurately represent Chinese national identity and promote cultural identification among its people. It’s a challenging task, but in this paper, I came across an innovative solution that combines generative adversarial network (GAN) technology to create a symbolic generation model based on visual images.
To improve the effectiveness of the GAN-based generative adversarial networks, we used a combination of pattern search regular terms and generator cross-loss functions to reduce the pattern collapse phenomenon commonly experienced with GANs. We also replaced the normal convolutional layer of the generator with a deep-space separable convolution to enhance real-time performance by reducing model parameters.
After conducting extensive testing on real datasets, the results were impressive! Our model was able to generate higher performance ethnic culture symbols while maintaining better temporal performance, which makes it a practical tool for creating cultural symbols that represent Chinese national identity.
1. Introduction
As a photographer who has spent a lot of time traveling throughout China, I’ve seen firsthand how important it is to have strong cultural symbols that represent the diverse ethnic groups that make up the Chinese nation. In recent years, there has been a push to promote the heritage protection and innovative intermingling of these cultures to create a more cohesive national community consciousness.
One of the ways that this is being achieved is through the establishment and highlighting of Chinese cultural symbols that are shared by all ethnic groups. This not only enhances the identification of all ethnic groups with Chinese culture, but it also plays a critical role in strengthening the theoretical connotation, narrative system, and discourse system of Chinese national community consciousness.
Furthermore, the establishment of these symbols and the image of the Chinese nation is a crucial step towards realizing the Chinese dream of the great rejuvenation of the Chinese nation. By casting a firm sense of Chinese national community, we can enhance the international image of China and promote a deeper understanding of its unique cultural heritage.
As a photographer, I believe that images play a critical role in creating and reinforcing cultural symbols. By capturing the beauty and diversity of China’s landscapes, people, and wildlife, we can help to promote a deeper appreciation of its rich cultural heritage and encourage a stronger sense of national community.
As a photographer who has spent a lot of time capturing the beauty and diversity of China, I understand the significance of cultural symbols that reflect the unique characteristics of Chinese culture and its diverse ethnic groups. These symbols embody the spiritual and cultural connotations of China’s rich and profound history.
In general, Chinese cultural symbols can be divided into two main categories. The first category includes natural symbols, such as mountains, rivers, stars, and constellations, which are found in China’s breathtaking landscapes. The second category includes man-made symbols, such as architecture and clothing, which reflect the creativity and craftsmanship of the Chinese people.
Theories regarding the origins of Chinese cultural symbols are varied and numerous. Some believe that these symbols originated from images created by ancient people for natural objects they worshiped, while others believe that they emerged due to practical utility or for decorative purposes.
Chinese national cultural symbols are particularly significant, as they embody the cultural connotations of Confucianism, political and ethical culture, and ethnic culture. For instance, Confucius culture and chess culture are integral parts of Chinese national culture and provide a rich source of cultural symbols.
As a photographer, I’m honored to have had the opportunity to capture some of these symbols in my photographs. Each image tells a story about the history, culture, and beauty of China, and I hope that they can help to promote a deeper appreciation and understanding of its unique cultural heritage.
Cultural symbols and visual images have become an important area of study in recent times, especially in the context of the Chinese community. However, to truly understand these symbols and images, it is essential to approach them through theoretical analysis and careful examination. This involves emphasizing the dynamic and process nature of these symbols, as well as the interconnection and transformation between figurative and conceptual elements.
Visual image research must also take a human-centered approach, with a focus on the active choices of practitioners and an examination of the relationship between symbolic images and intentional mental images. This approach emphasizes the multi-layered nature of cultural symbols and visual images and aims to construct a mutually inclusive identity within the holistic view of the Chinese national community.
It is also important to recognize that visual images should not be seen as the only or primary means of representation. Rather, they should be understood within the context of multisensory interventions, with attention given to expanding participation and sharing of cultural knowledge and concepts.
In the field of design practice, it is crucial to discover and utilize cultural symbols and visual image resources while respecting cultural knowledge and concepts. This involves implementing theoretical understanding to strengthen the sense of Chinese national community, with a focus on participation and sharing. By adopting these approaches, we can gain a deeper understanding and appreciation of cultural symbols and visual images within the Chinese community, and promote a more inclusive and diverse cultural identity.
The emergence of the Chinese nation as a self-conscious entity can be attributed to the confrontation between China and the Western powers in the last century, but it’s worth noting that this self-contained identity has been a result of thousands of years of historical evolution. The pluralistic pattern of the Chinese nation has been shaped by history, which is now a tangible reality. The Chinese national community consciousness is reflected in the unanimity, conceptual fit, and unity of will among the members of the nation on important issues related to the historical lineage, survival intention, and direction of development of the Chinese nation. Throughout the long process of Chinese civilization’s development, the people of all ethnic groups have contributed to the formation and evolution of Chinese culture, creating national images and cultural symbols that possess unique Chinese characteristics. These symbols are the concentrated embodiment of the living habits, spirituality, and values of the Chinese people of all ethnicities and have universal cultural and political identities.
The concept of national identity and community is an important aspect of any country’s culture, including China. To cultivate a strong sense of Chinese national community, one must first identify and appreciate Chinese cultural symbols and the image of the Chinese nation in everyday life.
The image of the Chinese nation is a stable and recognizable cultural symbol system that has evolved over time. It is a representation of the Chinese community to the outside world. Chinese cultural symbols and the image of the Chinese nation are both objective and subjective, tangible and intangible. They are not just abstract spiritual ideas but also concrete and sensual images that are rooted in real life.
To understand the national and cultural psychological structure behind these familiar national images and cultural symbols, we need to analyze them from both perceptual experience and rational thinking. This can be done systematically through cultural geography, cultural psychology, cultural politics, and other fields.
For example, the Chinese mountains, rivers, lakes, plants and flowers, festivals, cultural customs, and activities are typical representatives of the most Chinese image and cultural symbolism in daily life. By examining these symbols, we can explore the values, beliefs, and history of the Chinese people and their contributions to the formation and development of Chinese culture.
Casting a firm sense of Chinese national community requires not only the identification and understanding of these cultural symbols but also their appreciation and integration into daily life. By doing so, individuals can strengthen their connection with Chinese culture and contribute to the preservation and promotion of Chinese cultural heritage.
Typically, cultural symbols are comprised of four elements in their visual expression and application: “shape,” “meaning,” “color,” and “movement,” and should not be viewed as isolated symbols but rather integrated to function together. The visuals should convey meaning, mood, colors, and dynamics to communicate effectively with the audience. While aesthetics are important, an exceptional image should also be able to directly engage with the viewer. This paper introduces a technique for creating visual symbols using deep learning methods. The technique improves the mode collapse of the generative adversarial network (GAN) by combining a pattern search regularization term with the generator cross-loss function. Additionally, the generator’s common convolution layer in the network structure is replaced with deep-space separable convolution, reducing model parameters and enhancing real-time performance.
2. Current Status of Research
The development and evolution of Chinese national culture over thousands of years have resulted in a vast amount of symbolic resources. Although significant progress has been made in utilizing these materials for visual image design, there are persistent issues such as cultural insensitivity, overemphasis on form, and inadequate representation of the core message. It is crucial for art designers to possess a comprehensive understanding of national symbols to bridge modern visual communication design and national cultural symbols. They must delve into the cultural context and spiritual connotation behind the symbols, explore and extend them, reflect the regional features and national character they represent, and create design works with strong vitality that conform to contemporary aesthetic standards. [11, 12].
2.1. Image Generation Model
The field of image generation has made significant strides in recent years due to deep neural networks. Two commonly used generative models for image generation are Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). VAEs are probabilistic statistical models that consist of an encoding network and a decoding network for reconstructing the input image. They have advantages such as stable training and fast convergence, but the images generated by VAEs tend to look blurry overall. On the other hand, GANs implicitly capture the probability distribution of real images through adversarial learning between generators and discriminators. The generated images by GANs are clearer and more realistic, but they have significant drawbacks such as pattern collapse and training instability.
To stabilize the training process and improve the quality of the generated images, researchers have proposed effective methods such as conditional GANs. CGANs use auxiliary information like class labels to guide the generation of images. They have been widely used in various fields like super-resolution image generation, image style conversion, and image restoration.
Considering the excellent performance of adversarial networks in generating images, they are also used to complete the generation of target symbols. Designers can benefit from using these models to generate images with certain desired properties and to accurately convey their main idea. However, to ensure that the generated images have the required cultural significance and accurately represent cultural symbols, designers should have a clear understanding of the cultural background and spiritual connotation behind the symbols. By exploring, extending, and reconstructing these symbols, designers can create visually appealing design works with strong vitality that meet the aesthetics of contemporary visual images.
2.2. Symbol Generation Model
Symbol generation using deep neural networks is a rapidly evolving field that has seen significant progress in recent years. There are two broad categories of research in this field, the first being the use of discrete symbol properties, and the second being the integration of continuous auxiliary information into the generative model.
The first category of research involves encoding information such as gender, expression category, and hair color into the bottleneck layer of the conditional VAE model to generate diverse facial expression models. Another model proposed, called StarGAN, can inter-convert between different expression states such as angry and happy by classifying them into different domains. These methods generate high-quality symbolic expression images, but the encoded discrete symbolic attributes are not sufficient to describe the rich culture.
To address this limitation, the researchers explored how to integrate continuous auxiliary information into the generative model. The CDAAE model was proposed, which can separate symbolic information from each other. Given a reference symbol image, multiple cultural representations of the same culture can be generated by changing the FAU labels that represent different cultural strengths. The GAGAN model combines symbol shapes and GANs to make the generated symbols realistic, natural, and with specified symbol shapes. However, it does not provide any control over the generated symbolic information.
Concerning the generation of continuous symbols, direct linear interpolation of two different symbol shapes using symbol feature points has been proposed. These shapes are compressed into a one-dimensional coding vector using a fully connected network, which is then fed into the adversarial network to generate the continuous symbols. G2-GAN is another model that has been proposed for cultural symbol synthesis, which achieves the removal and generation of cultural symbols through two generative networks, respectively, and then achieves the conversion of arbitrary symbols.
In conclusion, the integration of continuous auxiliary information into generative models has shown promising results for generating cultural symbols with greater accuracy and richness. However, there is still much research to be done in this field, and the future holds much promise for advancing our understanding of how to effectively generate and manipulate cultural symbols using deep neural networks.
In recent years, deep neural networks have made significant advancements in image generation, particularly in the generation of cultural identity symbols. The generation of these symbols is of great importance as they represent cultural heritage and identity. In this context, a new model for the generation of Chinese cultural identity symbols has been proposed based on existing facial expression generation methods.
The proposed model combines pattern search regular terms with generator crossover loss functions based on GAN generative adversarial networks. This approach improves the stability and quality of the generated symbols. Furthermore, the normal convolutional layer of the generator in the network structure is replaced with a deep-space separable convolution. This modification leads to improved real-time performance by reducing the model parameters.
The proposed model provides a simple and effective solution to generate Chinese cultural identity symbols. It addresses some of the limitations of existing methods, such as pattern collapse and training instability. Additionally, the use of deep-space separable convolution improves the real-time performance of the model, making it more practical for real-world applications.
Overall, this work represents a significant contribution to the field of cultural identity symbol generation. The proposed model provides a promising avenue for future research and development in this area. With further refinement, this model could potentially be used in a wide range of applications, from preserving cultural heritage to enhancing visual communication design.
3. Methodology
3.1. GAN
Generative Adversarial Networks, or GANs, are a type of deep learning model that has been widely used for various tasks, including image generation, style transfer, and image restoration. GANs consist of two main components: the generative network and the discriminative network.
The discriminative network is responsible for distinguishing between real and fake samples. In contrast, the generative network is responsible for generating fake samples that resemble real samples as much as possible. The generative network aims to confuse the discriminator, while the discriminator aims to correctly classify the samples as real or fake.
During the training process, the two networks play a dynamic two-player game, where the generative network tries to generate more realistic samples to fool the discriminator, and the discriminator tries to correctly classify the samples as real or fake. This process continues until the discriminator can no longer distinguish between the real and fake samples.
The training process of GANs is iterative and involves optimizing the parameters of both the generative and discriminative networks. The objective function for GANs is a min-max game, where the generator aims to minimize the loss function while the discriminator aims to maximize it.
Overall, GANs have been shown to be effective in generating high-quality images that resemble real samples. With the continuous development of GANs and its variants, we can expect to see more applications of this powerful deep learning model in the future.
Generative Adversarial Networks, or GANs, are a type of deep learning model that has been widely used for various tasks, including image generation, style transfer, and image restoration. GANs consist of two main components: the generative network and the discriminative network.
The discriminative network is responsible for distinguishing between real and fake samples. In contrast, the generative network is responsible for generating fake samples that resemble real samples as much as possible. The generative network aims to confuse the discriminator, while the discriminator aims to correctly classify the samples as real or fake.
During the training process, the two networks play a dynamic two-player game, where the generative network tries to generate more realistic samples to fool the discriminator, and the discriminator tries to correctly classify the samples as real or fake. This process continues until the discriminator can no longer distinguish between the real and fake samples.
The training process of GANs is iterative and involves optimizing the parameters of both the generative and discriminative networks. The objective function for GANs is a min-max game, where the generator aims to minimize the loss function while the discriminator aims to maximize it.
Overall, GANs have been shown to be effective in generating high-quality images that resemble real samples. With the continuous development of GANs and its variants, we can expect to see more applications of this powerful deep learning model in the future.
