@inproceedings{2a03a41d3ac4481b9a2cb933429b151f,
title = "Causally Disentangled Generative Variational AutoEncoder",
abstract = "We present a new supervised learning technique for the Variational AutoEncoder (VAE) that allows it to learn a causally disentangled representation and generate causally disentangled outcomes simultaneously. We call this approach Causally Disentangled Generation (CDG). CDG is a generative model that accurately decodes an output based on a causally disentangled representation. Our research demonstrates that adding supervised regularization to the encoder alone is insufficient for achieving a generative model with CDG, even for a simple task. Therefore, we explore the necessary and sufficient conditions for achieving CDG within a specific model. Additionally, we introduce a universal metric for evaluating the causal disentanglement of a generative model. Empirical results from both image and tabular datasets support our findings.",
author = "An, {Seung Hwan} and Kyungwoo Song and Jeon, {Jong June}",
note = "Publisher Copyright: {\textcopyright} 2023 The Authors.; 26th European Conference on Artificial Intelligence, ECAI 2023 ; Conference date: 30-09-2023 Through 04-10-2023",
year = "2023",
month = sep,
day = "28",
doi = "10.3233/FAIA230258",
language = "English",
series = "Frontiers in Artificial Intelligence and Applications",
publisher = "IOS Press BV",
pages = "93--100",
editor = "Kobi Gal and Kobi Gal and Ann Nowe and Nalepa, {Grzegorz J.} and Roy Fairstein and Roxana Radulescu",
booktitle = "ECAI 2023 - 26th European Conference on Artificial Intelligence, including 12th Conference on Prestigious Applications of Intelligent Systems, PAIS 2023 - Proceedings",
address = "Netherlands",
}