TPAMI — 1 paper accepted!
One paper from the Center for Collaborative Intelligence at Tsinghua University (TsinghuaC3I) has been accepted to TPAMI.
TPAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence) is a flagship journal leading the field of artificial intelligence. It serves as a platform to share the latest ideas and technologies in computer vision and machine learning both in academia and industry.
Paper 1
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
Authors: Zhiyuan Ma, Yuzhu Zhang, Guoli Jia, Liangliang Zhao, Yichao Ma, Mingjie Ma, Gaofeng Liu, Kaiyan Zhang, Ning Ding, Jiajun Li,
Abstract: As one of the most popular and sought-after generative models in the recent years, diffusion models have sparked the interests of many researchers and steadily shown excellent advantage in various generative tasks such as image synthesis, video generation, molecule design, 3D scene rendering and multimodal generation, relying on their dense theoretical principles and reliable application practices. The remarkable success of these recent efforts on diffusion models comes largely from progressive design principles and efficient architecture, training, inference, and deployment methodologies. However, there has not been a comprehensive and in-depth review to summarize these principles and practices to help the rapid understanding and application of diffusion models. In this survey, we provide a new efficiency-oriented perspective on these existing efforts, which mainly focuses on the profound principles and efficient practices in architecture designs, model training, fast inference and reliable deployment, to guide further theoretical research, algorithm migration and model application for new scenarios in a reader-friendly way. https://github.com/ponyzym/Efficient-DMs-Survey
