In recent years, deep generative models have made significant progress, and diffusion models are particularly eye-catching, which effectively overcome many limitations of traditional generative models. Researchers from Hong Kong Chinese University, West Lake University, MIT and other institutions recently published a review paper in the IEEE TKDE journal, in-depth discussion of the latest progress of diffusion models and their wide application. This paper systematically summarizes the breakthrough results in this field and looks forward to future development trends.
Significant progress has been made in deep generative models, especially diffusion models that address the limitations of generative models. Hong Kong Chinese Language and Literature, West Lake University, MIT, etc. published a review paper on IEEE TKDE to discuss the progress and application of diffusion models in depth. Technologies such as knowledge distillation, improved training methods, and accelerated pre-training models have improved the efficiency of diffusion models. The diffusion model is not only successfully applied to image generation, but can also convert text into images and implement editing functions, demonstrating powerful technical application prospects.The advancement of diffusion model technology has brought new possibilities to the field of artificial intelligence, and its application in image generation and text-to-image conversion has brought huge development opportunities to all walks of life. In the future, with the continuous improvement and development of technology, the diffusion model will play an important role in more fields and promote the continuous progress of artificial intelligence technology.