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arXiv · 2406.07141

Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention

Avinash Kori·Francesco Locatello·Ainkaran Santhirasekaram·Francesca Toni·Ben Glocker·Fabio De Sousa Ribeiro
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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.

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Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, Fabio De Sousa Ribeiro. 2024-06-11. Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention. https://arxiv.org/abs/2406.07141

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