  We propose a probabilistic generative model for unsupervised learning of
structured, interpretable, object-based representations of visual scenes. We
use amortized variational inference to train the generative model end-to-end.
The learned representations of object location and appearance are fully
disentangled, and objects are represented independently of each other in the
latent space. Unlike previous approaches that disentangle location and
appearance, ours generalizes seamlessly to scenes with many more objects than
encountered in the training regime. We evaluate the proposed model on
multi-MNIST and multi-dSprites data sets.
