提出通用方法评估任意深度生成模型的解耦效果。
Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions
- 基于统计方法发现数据生成因子对应的潜在向量。
- 在两个数据集上验证了该方法优于传统对齐轴评估。
- 适合研究非轴对齐模型的解耦性，如AAE、WAE-MMD。
深度隐变量模型（DLVMs）旨在无监督地学习有意义的表示，使隐藏解释因子由独立的隐变量（即解耦）表示。变分自编码器（VAE）因其使用分解高斯分布建模后验分布，鼓励隐变量与隐空间坐标轴对齐，成为解耦分析的常用模型。近年来提出的多种度量指标均假设数据变化的解释因子与隐空间坐标轴对齐（主方向）。然而，其他模型如对抗自编码器（AAE）和WAE-MMD（匹配聚合后验与先验）中，隐变量可能并不对齐坐标轴。本文提出一种通用统计方法，可评估任意DLVM的解耦性。该方法不依赖坐标轴对齐假设，能发现代表数据生成因子的真实潜在向量。实验在两个数据集上验证了该方法的有效性。
原文摘要 · Abstract (English)
Deep latent variable models (DLVMs) are designed to learn meaningful representations in an unsupervised manner, such that the hidden explanatory factors are interpretable by independent latent variables (aka disentanglement). The variational autoencoder (VAE) is a popular DLVM widely studied in disentanglement analysis due to the modeling of the posterior distribution using a factorized Gaussian distribution that encourages the alignment of the latent factors with the latent axes. Several metrics have been proposed recently, assuming that the latent variables explaining the variation in data are aligned with the latent axes (cardinal directions). However, there are other DLVMs, such as the AAE and WAE-MMD (matching the aggregate posterior to the prior), where the latent variables might not be aligned with the latent axes. In this work, we propose a statistical method to evaluate disentanglement for any DLVMs in general. The proposed technique discovers the latent vectors representing the generative factors of a dataset that can be different from the cardinal latent axes. We empirically demonstrate the advantage of the method on two datasets.
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