Normalizing Flows for Domain Adaptation when Identifying Λ Hyperon Events
arxiv(2024)
摘要
This study focuses on the novel application of a normalizing flow as a method
of domain adaptation. Normalizing flows offer a way to transform data points
between two different distributions. The present study investigates a method of
transforming latent representations of physics data to a normal distribution
and then to a physics distribution again. The final distribution models a
simulated distribution. Following the transformation process, the data can be
classified by a neural network trained on labeled simulation data. The present
study succeeds in training two normalizing flows that can transform between
data (or simulation) and a Gaussian distribution.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要