Identifiability of deep generative models under mixture priors without auxiliary information

@article{Kivva2022IdentifiabilityOD,
  title={Identifiability of deep generative models under mixture priors without auxiliary information},
  author={Bohdan Kivva and Goutham Rajendran and Pradeep Ravikumar and Bryon Aragam},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.10044}
}
We prove identifiability of a broad class of deep latent variable models that (a) have universal approximation capabilities and (b) are the decoders of variational autoencoders that are commonly used in practice. Unlike existing work, our analysis does not require weak supervision, auxiliary information, or conditioning in the latent space. Recently, there has been a surge of works studying identifiability of such models. In these works, the main assumption is that along with the data, an… 

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