Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data.

We denote observed variables with gray color and latent variables with white color. Firstly, we introduce the concept of an uncontrolled style factor . Why do confident examples encourage content-style isolation? Calculate the loss using Eq. 1 and update networks; Output: The inference networks and classifier heads q It's essential to understand that although data augmentation cannot control all style factors, it still offers the benefit of "partial isolation". This approach, therefore, ensures that styles changes don't affect the derived content representation Calculate the loss using Eq. 2 and update networks; Output: The inference networks and classifier heads q Finally, confident and unlabeled examples are used to train the models based on the MixMatch algorithm.

A Introduction of do calculus. Do-calculus consists of three rules that help with identifying causal effects. Intuitively, Rule A.1 states when an observant can be omitted in estimating the interventional Theorem B.2. Suppose that the latent variable They assume that confounders exist but they are unobservable. Adapting C-Disentanglement to existing works further improve their performance.