• Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
• Page not in menu
This is a page not in th emain menu
Posts
• Diffusion Architectures Part I: Stability-Oriented Designs
Published:
This article explores how network architectures shape the stability of diffusion model training. We contrast U-Net and Transformer-based (DiT) backbones, analyzing how skip connections, residual scaling, and normalization influence gradient propagation across noise levels. By surveying stability-oriented innovations such as AdaGN, AdaLN-Zero, and skip pathway regulation, we reveal why architectural choices can determine whether training converges smoothly or collapses. The discussion provides both theoretical insights and practical design rules for building robust diffusion models.
• Analysis of the Stability and Efficiency of Diffusion Model Training
Published:
while diffusion models have revolutionized generative AI, their training challenges stem from a combination of resource intensity, optimization intricacies, and deployment hurdles. A stable training process ensures that the model produces good quality samples and converges efficiently over time without suffering from numerical instabilities.
• Unifying Discrete and Continuous Perspectives in Diffusion Models
Published:
Diffusion models have been shown to be a highly promising approach in the field of image generation. They treat image generation as two independent processes: the forward process, which transforms a complex data distribution into a known prior distribution (typically a standard normal distribution) by gradually injecting noise; and the reverse process, which transforms the prior distribution back into the complex data distribution by gradually removing the noise.
awards
books
patents
projects
publications
• Real-world image datasets for federated learning
• Authors: Jiahuan Luo, Xueyang Wu, Yun Luo, Anbu Huang, Yunfeng Huang, Yang Liu, Qiang Yang
• Published in Published in NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, 2019
• Privacy-preserving heterogeneous federated transfer learning
• Authors: Dashan Gao, Yang Liu, Anbu Huang, Ce Ju, Han Yu, Qiang Yang
• Published in Published in 2019 IEEE International Conference on Big Data (Big Data), 2019
• RPN: A residual pooling network for efficient federated learning
• Authors: Anbu Huang, Yuanyuan Chen, Yang Liu, Tianjian Chen, Qiang Yang
• Published in Published in the 24th European Conference on Artificial Intelligence, 2020
• Fedvision: An online visual object detection platform powered by federated learning
• Authors: Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, Qiang Yang
• Published in Published in the Proceedings of the AAAI Conference on Artificial Intelligence, 2020
• SolarNet: A Deep Learning Framework to Map Solar Plants In China From Satellite Imagery
• Authors: Xin Hou, Biao Wang, Wanqi Hu, Lei Yin, Anbu Huang, Haishan Wu
• Published in Published in ICLR 2020 Workshop on Tackling Climate Change with Machine Learning, 2020
• Dynamic backdoor attacks against federated learning
• Authors: Anbu Huang
• Published in ArXiv Preprint, 2020
• StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing
• Authors: Anbu Huang, Yang Liu, Tianjian Chen, Yongkai Zhou, Quan Sun, Hongfeng Chai, Qiang Yang
• Published in Published in ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2021
• Federated Learning-Powered Visual Object Detection for Safety Monitoring
• Authors: Yang Liu, Anbu Huang, Yun Luo, He Huang, Youzhi Liu, Yuanyuan Chen, Lican Feng, Tianjian Chen, Han Yu, Qiang Yang
• Published in Published in AI Magazine 2021, 2021
services
talks
• Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
• Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.