Research on the Application of Variational Autoencoder in Image Generation
The rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially i...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02001.pdf |
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| _version_ | 1825206580337967104 |
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| author | Liu Jianing |
| author_facet | Liu Jianing |
| author_sort | Liu Jianing |
| collection | DOAJ |
| description | The rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially in enhancing the quality and variety of produced images. This paper concentrates on applying Variational Autoencoders (VAEs) to image generation, a topic of increasing importance due to the model’s theoretical interpretability and stability. Through a detailed analysis of VAE principles, architecture, and applications, this research underscores the model’s capabilities in producing high-quality, varied images and its effectiveness in tasks such as image denoising and enhancement. The study also analysis the limitations of VAEs, like the inclination to generate blurry images, and discusses potential improvements, including hybrid models and enhanced loss functions. The results of this research enhance the comprehension of VAE’s capabilities and provide a foundation for future research aimed at advancing image generation technologies. |
| format | Article |
| id | doaj-art-9b15c0cd111243e5ac208afa26e22b85 |
| institution | Kabale University |
| issn | 2271-2097 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj-art-9b15c0cd111243e5ac208afa26e22b852025-02-07T08:21:10ZengEDP SciencesITM Web of Conferences2271-20972025-01-01700200110.1051/itmconf/20257002001itmconf_dai2024_02001Research on the Application of Variational Autoencoder in Image GenerationLiu Jianing0Ulster College, Shaanxi University of Science and TechnologyThe rapid development of artificial intelligence and deep learning has significantly influenced the domain of image creation, finding extensive applications in applications in fields like medical imaging, computer vision, and entertainment. Despite these advancements, challenges remain, especially in enhancing the quality and variety of produced images. This paper concentrates on applying Variational Autoencoders (VAEs) to image generation, a topic of increasing importance due to the model’s theoretical interpretability and stability. Through a detailed analysis of VAE principles, architecture, and applications, this research underscores the model’s capabilities in producing high-quality, varied images and its effectiveness in tasks such as image denoising and enhancement. The study also analysis the limitations of VAEs, like the inclination to generate blurry images, and discusses potential improvements, including hybrid models and enhanced loss functions. The results of this research enhance the comprehension of VAE’s capabilities and provide a foundation for future research aimed at advancing image generation technologies.https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02001.pdf |
| spellingShingle | Liu Jianing Research on the Application of Variational Autoencoder in Image Generation ITM Web of Conferences |
| title | Research on the Application of Variational Autoencoder in Image Generation |
| title_full | Research on the Application of Variational Autoencoder in Image Generation |
| title_fullStr | Research on the Application of Variational Autoencoder in Image Generation |
| title_full_unstemmed | Research on the Application of Variational Autoencoder in Image Generation |
| title_short | Research on the Application of Variational Autoencoder in Image Generation |
| title_sort | research on the application of variational autoencoder in image generation |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2025/01/itmconf_dai2024_02001.pdf |
| work_keys_str_mv | AT liujianing researchontheapplicationofvariationalautoencoderinimagegeneration |