Exploring the Role of the Bottleneck in Slot-Based Models Through
  Covariance Regularization
Andrew Stange Robert Lo Abishek Sridhar Kousik Rajesh
In this project we attempt to make slot-based models with an image
reconstruction objective competitive with those that use a feature
reconstruction objective on real world datasets. We propose a loss-based
approach to constricting the bottleneck of slot-based models, allowing
larger-capacity encoder networks to be used with Slot Attention without
producing degenerate stripe-shaped masks. We find that our proposed method
offers an improvement over the baseline Slot Attention model but does not reach
the performance of \dinosaur on the COCO2017 dataset. Throughout this project,
we confirm the superiority of a feature reconstruction objective over an image
reconstruction objective and explore the role of the architectural bottleneck
in slot-based models.