Causal Disentangled Representation Learning with VAE and Causal Flows
Keywords: disentanglement, representation learning, variational autoencoders, flows
Abstract: Disentangled representation learning aims to learn a low dimensional representation of data where each dimension corresponds to one underlying generative factor. Due to the causal relationships between generative factors in real-world situations, causal disentangled representation learning has received widespread attention. In this paper, we first propose a variant of autoregressive flows, called causal flows, which incorporate true causal structure of generative factors into the flows. Then, we design a new VAE model based on causal flows named *Causal Flows Variational Autoencoders (CauF-VAE)* to learn causally disentangled representations. We provide a theoretical analysis of the disentanglement identifiability of CauF-VAE by incorporating supervised information on the ground-truth factors. The performance of CauF-VAE is evaluated on both synthetic and real datasets, showing its capability of achieving causal disentanglement and performing intervention experiments. Moreover, CauF-VAE exhibits remarkable performance on downstream tasks and has the potential to learn true causal structure among factors.
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Primary Area: representation learning for computer vision, audio, language, and other modalities
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Submission Number: 4445
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