  We present a simple neural rendering architecture that helps variational
autoencoders (VAEs) learn disentangled representations. Instead of the
deconvolutional network typically used in the decoder of VAEs, we tile
(broadcast) the latent vector across space, concatenate fixed X- and
Y-"coordinate" channels, and apply a fully convolutional network with 1x1
stride. This provides an architectural prior for dissociating positional from
non-positional features in the latent distribution of VAEs, yet without
providing any explicit supervision to this effect. We show that this
architecture, which we term the Spatial Broadcast decoder, improves
disentangling, reconstruction accuracy, and generalization to held-out regions
in data space. It provides a particularly dramatic benefit when applied to
datasets with small objects. We also emphasize a method for visualizing learned
latent spaces that helped us diagnose our models and may prove useful for
others aiming to assess data representations. Finally, we show the Spatial
Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling
techniques and when incorporated improves their performance.
