The recently proposed identifiable variational autoencoder (iVAE) framework
provides a promising approach for learning latent independent components (ICs).
iVAEs use auxiliary covariates to build an identifiable generation structure
from covariates to ICs to observations, and the posterior network approximates
ICs given observations and covariates. Though the identifiability is appealing,
we show that iVAEs could have local minimum solution where observations and the
approximated ICs are independent given covariates.-a phenomenon we referred to
as the posterior collapse problem of iVAEs. To overcome this problem, we
develop a new approach, covariate-informed iVAE (CI-iVAE) by considering a
mixture of encoder and posterior distributions in the objective function. In
doing so, the objective function prevents the posterior collapse, resulting
latent representations that contain more information of the observations.
Furthermore, CI-iVAEs extend the original iVAE objective function to a larger
class and finds the optimal one among them, thus having tighter evidence lower
bounds than the original iVAE. Experiments on simulation datasets, EMNIST,
Fashion-MNIST, and a large-scale brain imaging dataset demonstrate the
effectiveness of our new method.

研究了可识别变分自编码器，在生成结构中使用辅助协变量来从协变量到观测值和独立组件（IC）中学习潜在的独立组件，并开发了一种称为 CI-iVAE 的新方法来解决后退化坍塌问题。