Combining Deep Generative Models with Generalized Linear Models for Image Generation and Repair Systems: Transitioning from Statistical Modeling to Deep Learning

Research Article
Open access

Combining Deep Generative Models with Generalized Linear Models for Image Generation and Repair Systems: Transitioning from Statistical Modeling to Deep Learning

Kaitian Chai 1*
  • 1 Australian National University, Canberra, Australian    
  • *corresponding author 1324408405@qq.com
ACE Vol.161
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-80590-155-6
ISBN (Online): 978-1-80590-156-3

Abstract

This study proposes a novel hybrid framework that integrates deep generative models and generalized linear models. Considering the limitation that generative models such as GAN and VAE can create realistic images but lack interpretation, we combine the statistical modeling capability of GLM with the abstract representation of deep learning by sharing the latent space. In the model architecture, the GLM branch ensures the consistency of the image structure, and the generative network is responsible for reconstructing semantic features. The two work collaboratively. The three types of random missing, center masking, and Gaussian noise degradation experiments conducted on the CelebA, cifar 10, and MNIST datasets show that this framework outperforms the single-model benchmark in terms of FID, PSNR, and SSIM metrics. Especially in medical imaging and cultural heritage restoration scenarios, the feature interpretation advantage provided by the GLM module is significant, and the influence of key parameters during the restoration process can be clearly traced. The experimental results confirm that the organic integration of statistical models and deep learning can not only improve the generation quality, but also open a new path for building reliable visual intelligence systems.

Keywords:

Deep Generative Models, Generalized Linear Models, GAN, VAE, Image Generation

Chai,K. (2025). Combining Deep Generative Models with Generalized Linear Models for Image Generation and Repair Systems: Transitioning from Statistical Modeling to Deep Learning. Applied and Computational Engineering,161,24-29.
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References

[1]. Kingma, D. P., Salimans, T., Poole, B., & Ho, J. (2021). Variational Diffusion Models. arXiv preprint arXiv:2107.00630.

[2]. Karras, T., Aittala, M., Hellsten, J., Laine, S., & Lehtinen, J. (2020). Training Generative Adversarial Networks with Limited Data. Advances in Neural Information Processing Systems, 33, 12104–12114.

[3]. Tao, M., Tang, H., Wu, F., Jing, X. Y., Bao, B. K., & Xu, C. (2020). DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis. arXiv preprint arXiv:2008.05865.

[4]. Liu, M. Y., Huang, X., Yu, J., Wang, T. C., & Mallya, A. (2020). Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications. arXiv preprint arXiv:2008.02793.

[5]. Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. arXiv preprint arXiv:2011.13456.

[6]. Liu, Y., & Zhang, Z. (2020). Diverse Image Generation via Self-Conditioned GANs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14286–14295.

[7]. Liu, M., & Wang, Y. (2020). Deep Generative Models for 3D Medical Image Synthesis. arXiv preprint arXiv:2011.01952.

[8]. Zhao, Z., Zhang, Z., Chen, T., Singh, S., & Zhang, H. (2020). Image Augmentations for GAN Training. arXiv preprint arXiv:2006.02595.

[9]. Tian, Y., Krishnan, D., & Isola, P. (2020). Contrastive Representation Distillation. arXiv preprint arXiv:1910.10699.​

[10]. Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2020). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, 7354–7363.

Cite this article

Chai,K. (2025). Combining Deep Generative Models with Generalized Linear Models for Image Generation and Repair Systems: Transitioning from Statistical Modeling to Deep Learning. Applied and Computational Engineering,161,24-29.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MSS 2025 Symposium: Automation and Smart Technologies in Petroleum Engineering

ISBN：978-1-80590-155-6(Print) / 978-1-80590-156-3(Online)
Editor：Mian Umer Shafiq
Conference website: https://2025.confmss.org
Conference date: 21 May 2025
Series: Applied and Computational Engineering
Volume number: Vol.161
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Kingma, D. P., Salimans, T., Poole, B., & Ho, J. (2021). Variational Diffusion Models. arXiv preprint arXiv:2107.00630.

[2]. Karras, T., Aittala, M., Hellsten, J., Laine, S., & Lehtinen, J. (2020). Training Generative Adversarial Networks with Limited Data. Advances in Neural Information Processing Systems, 33, 12104–12114.

[3]. Tao, M., Tang, H., Wu, F., Jing, X. Y., Bao, B. K., & Xu, C. (2020). DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis. arXiv preprint arXiv:2008.05865.

[4]. Liu, M. Y., Huang, X., Yu, J., Wang, T. C., & Mallya, A. (2020). Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applications. arXiv preprint arXiv:2008.02793.

[5]. Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. arXiv preprint arXiv:2011.13456.

[6]. Liu, Y., & Zhang, Z. (2020). Diverse Image Generation via Self-Conditioned GANs. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14286–14295.

[7]. Liu, M., & Wang, Y. (2020). Deep Generative Models for 3D Medical Image Synthesis. arXiv preprint arXiv:2011.01952.

[8]. Zhao, Z., Zhang, Z., Chen, T., Singh, S., & Zhang, H. (2020). Image Augmentations for GAN Training. arXiv preprint arXiv:2006.02595.

[9]. Tian, Y., Krishnan, D., & Isola, P. (2020). Contrastive Representation Distillation. arXiv preprint arXiv:1910.10699.​

[10]. Zhang, H., Goodfellow, I., Metaxas, D., & Odena, A. (2020). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, 7354–7363.

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