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Object-centric causal representation learning
Amin Mansouri · Jason Hartford · Kartik Ahuja · Yoshua Bengio

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in Symmetry and Geometry in Neural Representations (NeurReps) »
Event URL: https://openreview.net/forum?id=RaIy9t062cD »
There has been significant recent progress in causal representation learning that has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence class). Common to all of these approaches is the assumption that (1) the latent variables are $d-$dimensional vectors, and (2) that the observations are the output of some injective observation function of these latent variables. While these assumptions appear benign–they amount to assuming that any changes in the latent space are reflected in the observation space, and that we can use standard encoders to infer the latent variables–we show that when the observations are of multiple objects, the observation function is no longer injective, and disentanglement fails in practice. We can address this failure by combining recent developments in object-centric learning and causal representation learning. By modifying the Slot Attention architecture \citep{Locatello2020}, we develop an object-centric architecture that leverages weak supervision from sparse perturbations to disentangle each object's properties. We argue that this approach is more data-efficient in the sense that it requires significantly fewer perturbations than a comparable approach that encodes to a Euclidean space and, we show that this approach successfully disentangles the properties of a set of objects in a series of simple image-based disentanglement experiments.
Keywords: Representation Learning · Disentanglement · object centric learning

Author Information

Amin Mansouri (Montreal Institute for Learning Algorithms, University of Montreal, Université de Montréal)
Jason Hartford (Montreal Institute for Learning Algorithms, University of Montreal, University of Montreal)
Kartik Ahuja (Mila)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

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