  Unsupervised extraction of objects from low-level visual data is an important
goal for further progress in machine learning. Existing approaches for
representing objects without labels use structured generative models with
static images. These methods focus a large amount of their capacity on
reconstructing unimportant background pixels, missing low contrast or small
objects. Conversely, we present a new method that avoids losses in pixel space
and over-reliance on the limited signal a static image provides. Our approach
takes advantage of objects' motion by learning a discriminative,
time-contrastive loss in the space of slot representations, attempting to force
each slot to not only capture entities that move, but capture distinct objects
from the other slots. Moreover, we introduce a new quantitative evaluation
metric to measure how "diverse" a set of slot vectors are, and use it to
evaluate our model on 20 Atari games.
