Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
<|reference_start|>Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention: 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.<|reference_end|>
@article{kori2024identifiable,
    title={Identifiable Object-Centric Representation Learning via Probabilistic
  Slot Attention},
    author={Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca
  Toni, Ben Glocker, Fabio De Sousa Ribeiro},
    journal={arXiv preprint arXiv:2406.07141},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07141},
    primaryClass={cs.LG cs.AI}
}