تقرير
Learning Hierarchical Priors in VAEs
| العنوان: | Learning Hierarchical Priors in VAEs |
|---|---|
| المؤلفون: | Klushyn, Alexej, Chen, Nutan, Kurle, Richard, Cseke, Botond, van der Smagt, Patrick |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning |
| الوصف: | We propose to learn a hierarchical prior in the context of variational autoencoders to avoid the over-regularisation resulting from a standard normal prior distribution. To incentivise an informative latent representation of the data, we formulate the learning problem as a constrained optimisation problem by extending the Taming VAEs framework to two-level hierarchical models. We introduce a graph-based interpolation method, which shows that the topology of the learned latent representation corresponds to the topology of the data manifold---and present several examples, where desired properties of latent representation such as smoothness and simple explanatory factors are learned by the prior. Comment: Published at NeurIPS 2019 (spotlight) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1905.04982 |
| رقم الأكسشن: | edsarx.1905.04982 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |