Gacs-Korner 公共信息变分自编码器
Gacs-Korner Common Information Variational Autoencoder
AI总结：
该研究提出一种可经验近似的公共信息概念（含Gács-Körner公共信息特例），通过修改传统变分自编码器实现高维数据中公共与独有信息的划分量化，在图像视频等数据及已知真实潜在因子数据集上验证了有效性。
摘要：
我们提出了一种公共信息概念，能够量化并分离两个随机变量之间共享的信息与各自独有的信息。该公共信息通过一族函数上的优化问题定义，且可将Gács-Körner公共信息作为特例恢复。重要的是，我们的概念能够利用底层数据分布的样本进行经验近似。随后，我们提供了一种通过对传统变分自编码器（VAE）进行简单修改，来划分并量化公共与独有信息的方法。实验结果表明，我们的公式能够在图像、视频等高维数据上学习到具有语义意义的公共与独有变异因子。此外，在真实潜在因子已知的数据集上，我们证明了该方法可以准确量化随机变量之间的公共信息。
英文摘要：
We propose a notion of common information that allows one to quantify and separate the information that is shared between two random variables from the information that is unique to each. Our notion of common information is defined by an optimization problem over a family of functions and recovers the Gács-Körner common information as a special case. Importantly, our notion can be approximated empirically using samples from the underlying data distribution. We then provide a method to partition and quantify the common and unique information using a simple modification of a traditional variational auto-encoder. Empirically, we demonstrate that our formulation allows us to learn semantically meaningful common and unique factors of variation even on high-dimensional data such as images and videos. Moreover, on datasets where ground-truth latent factors are known, we show that we can accurately quantify the common information between the random variables.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
