8 citations · 11 across the 3 of their papers we have counts for
3 papers
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
Đorđe Miladinović, Kumar Shridhar, Kushal Jain +4
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.…
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
João B. S. Carvalho, João A. Santinha, Đorđe Miladinović +1
In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation. The quality of this image segmentation ste…
Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling
Đorđe Miladinović, Aleksandar Stanić, Stefan Bauer +2
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) a…