Spatial Dependency Networks: Neural Layers for Improved Generative Image
Modeling

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling

16 March 2021

DJordje Miladinović

Aleksandar Stanić

Stefan Bauer

Jürgen Schmidhuber

Papers citing "Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling"

7 / 7 papers shown

Title
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs Ðorðe Miladinovic Kumar Shridhar Kushal Kumar Jain Max B. Paulus J. M. Buhmann Mrinmaya Sachan Carl Allen DRL 28 5 0 26 Sep 2022
Structured Uncertainty in the Observation Space of Variational Autoencoders James A. G. Langley M. Monteiro Charles Jones Nick Pawlowski Ben Glocker CML OOD BDL DRL 39 2 0 25 May 2022
Boxhead: A Dataset for Learning Hierarchical Representations Yukun Chen Andrea Dittadi Frederik Trauble Stefan Bauer Bernhard Schölkopf CML 99 2 0 07 Oct 2021
Exploring the Latent Space of Autoencoders with Interventional Assays Felix Leeb Stefan Bauer M. Besserve Bernhard Schölkopf DRL 51 17 0 30 Jun 2021
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation João B. S. Carvalho J. Santinha DJordje Miladinović J. M. Buhmann SSeg 24 0 0 22 Mar 2021
On Disentangled Representations Learned From Correlated Data Frederik Trauble Elliot Creager Niki Kilbertus Francesco Locatello Andrea Dittadi Anirudh Goyal Bernhard Schölkopf Stefan Bauer OOD CML 29 115 0 14 Jun 2020
Pixel Recurrent Neural Networks Aaron van den Oord Nal Kalchbrenner Koray Kavukcuoglu SSeg GAN 272 2,553 0 25 Jan 2016