LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multi-modal Foundation Models
<|reference_start|>LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multi-modal Foundation Models: 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.<|reference_end|>
@article{zhu2024latentexplainer:,
    title={LatentExplainer: Explaining Latent Representations in Deep Generative
  Models with Multi-modal Foundation Models},
    author={Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang
  Zhao},
    journal={arXiv preprint arXiv:2406.14862},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14862},
    primaryClass={cs.LG cs.CL cs.CV}
}