  Learning modular object-centric representations is crucial for systematic
generalization. Existing methods show promising object-binding capabilities
empirically, but theoretical identifiability guarantees remain relatively
underdeveloped. Understanding when object-centric representations can
theoretically be identified is crucial for scaling slot-based methods to
high-dimensional images with correctness guarantees. To that end, we propose a
probabilistic slot-attention algorithm that imposes an aggregate mixture prior
over object-centric slot representations, thereby providing slot
identifiability guarantees without supervision, up to an equivalence relation.
We provide empirical verification of our theoretical identifiability result
using both simple 2-dimensional data and high-resolution imaging datasets.
