Open Access
Issue
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
Article Number 02038
Number of page(s) 6
Section Machine Learning, Deep Learning, and Applications
DOI https://doi.org/10.1051/itmconf/20257302038
Published online 17 February 2025
  1. AR. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-Resolution Image Synthesis with Latent Diffusion Models, arXiv preprint arXiv:2112.10752 (2022). [Google Scholar]
  2. L. Tsung-Yi, M. Michael, J. B. Serge, D. B. Lubomir, B. G. Ross, H. James, P. Pietr, R. Deva, D. Piotr, and C. Lawrence Zitnick. Microsoft COCO: common objects in context. CoRR. 6, 7, 27 (2014). [Google Scholar]
  3. C. Saharia, J. Ho, W. Chan, D. J. Fleet, M. Norouzi, Image Super-Resolution via Iterative Refinement, arXiv preprint arXiv:2104.07636 (2022). [Google Scholar]
  4. E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, L. Wang, L. Wang, W. Chen, LoRA: Low-Rank Adaptation of Large Language Models, arXiv preprint arXiv:2106.09685 (2021). [Google Scholar]
  5. H. Cao, C. Tan, Z. Gao, Y. Xu, G. Chen, P. A. Heng, S. Z. Li, A survey on generative diffusion models, IEEE Transactions on Knowledge and Data Engineering (2024). [Google Scholar]
  6. J. Ho, A. Jain, P. Abbeel, Denoising Diffusion Probabilistic Models, Advances in Neural Information Processing Systems 33, 6840-6851 (2020). [Google Scholar]
  7. D. P. Kingma, M. Welling, Auto-Encoding Variational Bayes, Proceedings of the International Conference on Learning Representations (ICLR) (2014). [Google Scholar]
  8. Y. Zhou, et al., Efficient Fine-tuning of Pre-trained Models with Low-Rank Adaptation, In Proceedings of the AAAI Conference on Artificial Intelligence (2022). [Google Scholar]
  9. A. Borji, Generated faces in the wild: Quantitative comparison of stable diffusion, midjourney and dall-e 2, arXiv preprint arXiv:2210.00586 (2022). [Google Scholar]
  10. Y. Zhou, B. Liu, Y. Zhu, X. Yang, C. Chen, J. Xu, Shifted diffusion for text-to-image generation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10157-10166 (2023). [Google Scholar]
  11. M. Chhabra, K. Manasa, A. Devraj, Impact of Data Sample Size on Machine Learning Model Accuracy, International Journal of Data Science and Analytics 7(4), 150–162 (2020). [Google Scholar]
  12. A. Koshti, Challenges of Small Sample Sizes in Deep Learning Models, Proceedings of the Neural Information Processing Symposium (NIPS) 34, 3001–3012 (2022). [Google Scholar]

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