  We introduce CellSegmenter, a structured deep generative model and an
amortized inference framework for unsupervised representation learning and
instance segmentation tasks. The proposed inference algorithm is convolutional
and parallelized, without any recurrent mechanisms, and is able to resolve
object-object occlusion while simultaneously treating distant non-occluding
objects independently. This leads to extremely fast training times while
allowing extrapolation to arbitrary number of instances. We further introduce a
transparent posterior regularization strategy that encourages scene
reconstructions with fewest localized objects and a low-complexity background.
We evaluate our method on a challenging synthetic multi-MNIST dataset with a
structured background and achieve nearly perfect accuracy with only a few
hundred training epochs. Finally, we show segmentation results obtained for a
cell nuclei imaging dataset, demonstrating the ability of our method to provide
high-quality segmentations while also handling realistic use cases involving
large number of instances.
