TY - CONF A1 - Lavda, Frantzeska A1 - Gregorová, Magda A1 - Kalousis, Alexandros A2 - De Giacomo, Giuseppe A2 - Catalá, Alejandro A2 - Dilkina, Bistra A2 - Milano, Michela A2 - Barro, Senén A2 - Bugarı́n, Alberto A2 - Lang, Jérôme T1 - Improving VAE Generations of Multimodal Data Through Data-Dependent Conditional Priors T2 - ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020 - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) N2 - One of the major shortcomings of variational autoencoders is the inability to produce generations from the individual modalities of data originating from mixture distributions. This is primarily due to the use of a simple isotropic Gaussian as the prior for the latent code in the ancestral sampling procedure for the data generations. We propose a novel formulation of variational autoencoders, conditional prior VAE (CP-VAE), which learns to differentiate between the individual mixture components and therefore allows for generations from the distributional data clusters. We assume a two-level generative process with a continuous (Gaussian) latent variable sampled conditionally on a discrete (categorical) latent component. The new variational objective naturally couples the learning of the posterior and prior conditionals, and the learning of the latent categories encoding the multimodality of the original data in an unsupervised manner. The data-dependent conditional priors are then used to sample the continuous latent code when generating new samples from the individual mixture components corresponding to the multimodal structure of the original data. Our experimental results illustrate the generative performance of our new model comparing to multiple baselines. Y1 - 2020 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/4959 VL - 325 SP - 1254 EP - 1261 ER -