  Deep generative models like VAEs and diffusion models have advanced various
generation tasks by leveraging latent variables to learn data distributions and
generate high-quality samples. Despite the field of explainable AI making
strides in interpreting machine learning models, understanding latent variables
in generative models remains challenging. This paper introduces
LatentExplainer, a framework for automatically generating semantically
meaningful explanations of latent variables in deep generative models.
LatentExplainer tackles three main challenges: inferring the meaning of latent
variables, aligning explanations with inductive biases, and handling varying
degrees of explainability. By perturbing latent variables and interpreting
changes in generated data, the framework provides a systematic approach to
understanding and controlling the data generation process, enhancing the
transparency and interpretability of deep generative models. We evaluate our
proposed method on several real-world and synthetic datasets, and the results
demonstrate superior performance in generating high-quality explanations of
latent variables.
