@inproceedings{19007,
  abstract     = {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.
},
  author       = {Kori, Avinash and Locatello, Francesco and Santhirasekaram, Ainkaran and Toni, Francesca and Glocker, Ben and De Sousa Ribeiro, Fabio},
  booktitle    = {38th Conference on Neural Information Processing Systems},
  location     = {Vancouver, Canada},
  publisher    = {Neural Information Processing Systems Foundation},
  title        = {{Identifiable object-centric representation learning via probabilistic slot attention}},
  volume       = {37},
  year         = {2024},
}