  Unsupervised multi-object representation learning depends on inductive biases
to guide the discovery of object-centric representations that generalize.
However, we observe that methods for learning these representations are either
impractical due to long training times and large memory consumption or forego
key inductive biases. In this work, we introduce EfficientMORL, an efficient
framework for the unsupervised learning of object-centric representations. We
show that optimization challenges caused by requiring both symmetry and
disentanglement can in fact be addressed by high-cost iterative amortized
inference by designing the framework to minimize its dependence on it. We take
a two-stage approach to inference: first, a hierarchical variational
autoencoder extracts symmetric and disentangled representations through
bottom-up inference, and second, a lightweight network refines the
representations with top-down feedback. The number of refinement steps taken
during training is reduced following a curriculum, so that at test time with
zero steps the model achieves 99.1% of the refined decomposition performance.
We demonstrate strong object decomposition and disentanglement on the standard
multi-object benchmark while achieving nearly an order of magnitude faster
training and test time inference over the previous state-of-the-art model.
