使用条件归一化流学习似然性

This research presents a model based on Conditional Normalizing Flows for learning the likelihood of complex data distributions. By incorporating conditional variables, the model can generate data under different conditions, enhancing the flexibility and expressiveness of generative models. The effectiveness of this approach is demonstrated across multiple datasets, particularly in high-dimensional data scenarios where Conditional Normalizing Flows significantly improve generation quality. Furthermore, experimental results indicate that the model excels in both inference and generation tasks, showcasing its broad application potential, especially in image generation and natural language processing.