@inproceedings{c994320c212b4782868143abd764b79b,
title = "Compositional Conditional Diffusion Model",
abstract = "Generative model often struggle to produce data beyond the training distribution. To address this, we explored various methods for unseen data generation, eventually focusing on compositional zero-shot image generation. By guiding the generation model with compositional class labels, we achieved better control over the generation process. Our model can generate unseen images whose compositional labels are not appear in the training set. While large language models like GPT-4 and image generation models like DALL-E offer similar zero-shot generation capabilities, our research emphasizes domain-specific zero-shot generation using smaller models. Through a series of tasks, we demonstrated the effectiveness of compositional zero-shot image generation across various complexities, showcasing its potential in contemporary machine learning.",
keywords = "Compositional Zero-shot Learning, Diffusion Model, Image Synthesis",
author = "Lai, {Shih Lun} and Chen, {Pin Chuan} and Ma, {Ching Wen}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 10th International Conference on Applied System Innovation, ICASI 2024 ; Conference date: 17-04-2024 Through 21-04-2024",
year = "2024",
doi = "10.1109/ICASI60819.2024.10547979",
language = "English",
series = "Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "377--379",
editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Prior, {Stephen D.}",
booktitle = "Proceedings of the 2024 10th International Conference on Applied System Innovation, ICASI 2024",
address = "美國",
}