Deep Discrete Encoders: Identifiable Deep Generative Models for Rich Data with Discrete Latent Layers
深度离散编码器：具有离散潜在层的可识别深度生成模型
In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose interpretable deep generative models for rich data types…