SPATIAL DEPENDENCY NETWORKS: NEURAL LAYERS FOR IMPROVED GENERATIVE IMAGE MODELING

Dorde Miladinović*, Aleksandar Stanić, Stefan Bauer, Jürgen Schmidhuber, Joachim M. Buhmann

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

How to improve generative modeling by better exploiting spatial regularities and coherence in images? We introduce a novel neural network for building image generators (decoders) and apply it to variational autoencoders (VAEs). In our spatial dependency networks (SDNs), feature maps at each level of a deep neural net are computed in a spatially coherent way, using a sequential gating-based mechanism that distributes contextual information across 2-D space. We show that augmenting the decoder of a hierarchical VAE by spatial dependency layers considerably improves density estimation over baseline convolutional architectures and the state-of-the-art among the models within the same class. Furthermore, we demonstrate that SDN can be applied to large images by synthesizing samples of high quality and coherence. In a vanilla VAE setting, we find that a powerful SDN decoder also improves learning disentangled representations, indicating that neural architectures play an important role in this task. Our results suggest favoring spatial dependency over convolutional layers in various VAE settings. The accompanying source code is given at: https://github.com/djordjemila/sdn.

Original languageEnglish (US)
StatePublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online
Duration: May 3 2021May 7 2021

Conference

Conference9th International Conference on Learning Representations, ICLR 2021
CityVirtual, Online
Period05/3/2105/7/21

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science Applications
  • Education
  • Linguistics and Language

Fingerprint

Dive into the research topics of 'SPATIAL DEPENDENCY NETWORKS: NEURAL LAYERS FOR IMPROVED GENERATIVE IMAGE MODELING'. Together they form a unique fingerprint.

Cite this