@inproceedings{
kori2024identifiable,
title={Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention},
author={Avinash Kori and Francesco Locatello and Ainkaran Santhirasekaram and Francesca Toni and Ben Glocker and Fabio De Sousa Ribeiro},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=qmoVQbwmCY}
}
Learning modular object-centric representations is said to be 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 important 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.
Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention
Avinash Kori Francesco Locatello Ainkaran Santhirasekaram Francesca Toni Ben Glocker Fabio De Sousa Ribeiro