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Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
May 9, 2024, 4:42 a.m. | Di Fan, Yannian Kou, Chuanhou Gao
cs.LG updates on arXiv.org arxiv.org
Abstract: Disentangled representation learning aims to learn low-dimensional representations of data, where each dimension corresponds to an underlying generative factor. Currently, Variational Auto-Encoder (VAE) are widely used for disentangled representation learning, with the majority of methods assuming independence among generative factors. However, in real-world scenarios, generative factors typically exhibit complex causal relationships. We thus design a new VAE-based framework named Disentangled Causal Variational Auto-Encoder (DCVAE), which includes a variant of autoregressive flows known as causal flows, …
abstract arxiv auto causal cs.lg data encoder flow generative however learn low representation representation learning stat.me type vae world
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