Open Access
| Issue |
ITM Web Conf.
Volume 84, 2026
2026 International Conference on Advent Trends in Computational Intelligence and Data Science (ATCIDS 2026)
|
|
|---|---|---|
| Article Number | 03027 | |
| Number of page(s) | 7 | |
| Section | Large Language Models, Generative AI, and Multimodal Learning | |
| DOI | https://doi.org/10.1051/itmconf/20268403027 | |
| Published online | 06 April 2026 | |
- W. Xia, Y. Zhang, Y. Yang, J. H. Xue, B. Zhou, and M. H. Yang. Gan inversion: A survey. IEEE transactions on pattern analysis and machine intelligence, 45(3), pp.3121–3138 (2022) [Google Scholar]
- J. Jenkins, and K. Roy. Exploring deep convolutional generative adversarial networks (DCGAN) in biometric systems: a survey study. Discover Artificial Intelligence, 4(1), p.42 (2024) [Google Scholar]
- Q. Wang, X. Zhou, C. Wang, Z. Liu, J. Huang, Y. Zhou, C. Li, H. Zhuang, and J. Z. Cheng. WGAN-based synthetic minority over-sampling technique: Improving semantic fine-grained classification for lung nodules in CT images. IEEE Access, 7, pp.18450–18463 (2019) [Google Scholar]
- T. Xia, and L. Liu. LSN-GAN: A Novel Least Square Gradient Normalization for Generative Adversarial Networks. In 2024 IEEE 4th International Conference on Software Engineering and Artificial Intelligence (SEAI) (pp. 343-347). IEEE (2024) [Google Scholar]
- S. Odaibo. Tutorial: Deriving the standard variational autoencoder (vae) loss function. arXiv preprint arXiv:1907.08956 (2019) [Google Scholar]
- C. P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, G. Desjardins, and A. Lerchner. Understanding disentangling in $\beta $-VAE. arXiv preprint arXiv:1804.03599 (2018) [Google Scholar]
- A. Razavi, A. Van den Oord, and O. Vinyals. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32 (2019) [Google Scholar]
- F. A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah. Diffusion models in vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 45(9), pp.10850–10869 (2023) [Google Scholar]
- A. Alblwi, S. Makkawy, and K. E. Barner. D-DDPM: Deep Denoising Diffusion Probabilistic Models for Lesion Segmentation and Data Generation in Ultrasound Imaging. IEEE Access (2025) [Google Scholar]
- D. Bolya, and J. Hoffman. Token merging for fast stable diffusion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4599-4603) (2023) [Google Scholar]
- Y. Yu, W. Zhang, and Y. Deng. Frechet inception distance (fid) for evaluating gans. China University of Mining Technology Beijing Graduate School, 3(11) (2021) [Google Scholar]
- S. Barratt, and R. Sharma. A note on the inception score. arXiv preprint arXiv:1801.01973 (2018) [Google Scholar]
- M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly. Assessing generative models via precision and recall. Advances in neural information processing systems, 31 (2018) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.