# Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

@article{Hlv2021DisentanglingIF, title={Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA}, author={Hermanni H{\"a}lv{\"a} and Sylvain Le Corff and Luc Leh'ericy and Jonathan So and Yongjie Zhu and Elisabeth Gassiat and Aapo Hyv{\"a}rinen}, journal={ArXiv}, year={2021}, volume={abs/2106.09620} }

We introduce a new general identifiable framework for principled disentanglement referred to as Structured Nonlinear Independent Component Analysis (SNICA). Our contribution is to extend the identifiability theory of deep generative models for a very broad class of structured models. While previous works have shown identifiability for specific classes of time-series models, our theorems extend this to more general temporal structures as well as to models with more complex structures such as…

## One Citation

Identifiable Variational Autoencoders via Sparse Decoding

- Computer Science, MathematicsArXiv
- 2021

The Sparse VAE is identifiable: given data drawn from the model, there exists a uniquely optimal set of factors, and it is found that it recovers meaningful latent factors and has smaller heldout reconstruction error than related methods.

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