Auxiliary Guided Autoregressive Variational Autoencoders - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

Auxiliary Guided Autoregressive Variational Autoencoders


Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. In contrast, prior work on such hybrid models needed to limit the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our approach results in models with meaningful latent variable representations, and which rely on powerful autoregressive decoders to model image details. Our model generates qualitatively convincing samples, and yields state-of-the-art quantitative results.
Fichier principal
Vignette du fichier
paper_ecml.pdf (4.15 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01652881 , version 1 (30-11-2017)
hal-01652881 , version 2 (19-07-2018)



Thomas Lucas, Jakob Verbeek. Auxiliary Guided Autoregressive Variational Autoencoders. ECML-PKDD 2018 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2018, Dublin, Ireland. pp.443-458, ⟨10.1007/978-3-030-10925-7_27⟩. ⟨hal-01652881v2⟩
641 View
699 Download



Gmail Facebook X LinkedIn More