SCADI: Self-supervised Causal Disentanglement in Latent Variable Models
Heejeong Nam
Causal disentanglement has great potential for capturing complex situations.
However, there is a lack of practical and efficient approaches. It is already
known that most unsupervised disentangling methods are unable to produce
identifiable results without additional information, often leading to randomly
disentangled output. Therefore, most existing models for disentangling are
weakly supervised, providing information about intrinsic factors, which incurs
excessive costs. Therefore, we propose a novel model, SCADI(SElf-supervised
CAusal DIsentanglement), that enables the model to discover semantic factors
and learn their causal relationships without any supervision. This model
combines a masked structural causal model (SCM) with a pseudo-label generator
for causal disentanglement, aiming to provide a new direction for
self-supervised causal disentanglement models.