论文标题
协变信息的表示为预防IVAE后塌陷
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE
论文作者
论文摘要
最近提出的可识别的变分自动编码器（IVAE）框架为学习潜在独立组件（ICS）提供了有希望的方法。 Ivaes使用辅助协变量来构建从协变量到IC到观测值的可识别生成结构，后验网络近似于ICS给定观测和协变量。尽管可识别性很有吸引力，但我们表明，IVAE可能具有局部最小解决方案，在观察结果和近似IC是独立的，给定协变量。-我们称为IVAES后倒塌问题。为了克服这个问题，我们通过考虑目标函数中编码器和后验分布的混合物来开发一种新方法，即协变量的IVAE（CI-IVAE）。在此过程中，目标函数阻止了后置崩溃，从而导致了包含更多观测信息的潜在表示。此外，Ci-ivaes将原始IVAE目标函数扩展到较大的类别，并找到了其中的最佳选择，因此比原始IVAE具有更紧密的证据。在模拟数据集，Emnist，Fashion-Mnist和大型脑成像数据集上进行的实验证明了我们新方法的有效性。
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.
