Object-centric architectures enable efficient causal representation learning

• URL: http://arxiv.org/abs/2310.19054v1
• Date: Sun, 29 Oct 2023 16:01:03 GMT
• Title: Object-centric architectures enable efficient causal representation learning
• Authors: Amin Mansouri, Jason Hartford, Yan Zhang, Yoshua Bengio
• Abstract summary: We show that when the observations are of multiple objects, the generative function is no longer injective and disentanglement fails in practice. We develop an object-centric architecture that leverages weak supervision from sparse perturbations to disentangle each object's properties. 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.
• Score: 51.6196391784561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal representation learning 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 represented as $d$-dimensional vectors, and (2) that the observations are the output of some injective generative function of these latent variables. While these assumptions appear benign, we show that when the observations are of multiple objects, the generative 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 arXiv:2006.15055, we develop an object-centric architecture that leverages weak supervision from sparse perturbations to disentangle each object's properties. 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.

Related papers

• A Sparsity Principle for Partially Observable Causal Representation Learning [28.25303444099773]
  Causal representation learning aims at identifying high-level causal variables from perceptual data. We focus on learning from unpaired observations from a dataset with an instance-dependent partial observability pattern. We propose two methods for estimating the underlying causal variables by enforcing sparsity in the inferred representation.
  arXiv  Detail & Related papers  (2024-03-13T08:40:49Z)
• Explicitly Disentangled Representations in Object-Centric Learning [0.0]
  We propose a novel architecture that biases object-centric models toward disentangling shape and texture components. In particular, we propose a novel architecture that biases object-centric models toward disentangling shape and texture components.
  arXiv  Detail & Related papers  (2024-01-18T17:22:11Z)
• Cycle Consistency Driven Object Discovery [75.60399804639403]
  We introduce a method that explicitly optimize the constraint that each object in a scene should be associated with a distinct slot. By integrating these consistency objectives into various existing slot-based object-centric methods, we showcase substantial improvements in object-discovery performance. Our results suggest that the proposed approach not only improves object discovery, but also provides richer features for downstream tasks.
  arXiv  Detail & Related papers  (2023-06-03T21:49:06Z)
• Generative Models of Huge Objects [6.7086472013021305]
  Indistinguishability from a single object is motivated by the study of generative models in learning theory and regularity lemmas in graph theory. We provide a learning algorithm for huge indistinguishable objects in several natural settings. Results rely on notions and techniques from a variety of areas including learning theory, complexity theory, cryptography, and game theory.
  arXiv  Detail & Related papers  (2023-02-24T18:58:11Z)
• Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation [88.14365009076907]
  Iterative refinement is a useful paradigm for representation learning. We develop an implicit differentiation approach that improves the stability and tractability of training.
  arXiv  Detail & Related papers  (2022-07-02T10:00:35Z)
• Invariant Causal Mechanisms through Distribution Matching [86.07327840293894]
  In this work we provide a causal perspective and a new algorithm for learning invariant representations. Empirically we show that this algorithm works well on a diverse set of tasks and in particular we observe state-of-the-art performance on domain generalization.
  arXiv  Detail & Related papers  (2022-06-23T12:06:54Z)
• Complex-Valued Autoencoders for Object Discovery [62.26260974933819]
  We propose a distributed approach to object-centric representations: the Complex AutoEncoder. We show that this simple and efficient approach achieves better reconstruction performance than an equivalent real-valued autoencoder on simple multi-object datasets. We also show that it achieves competitive unsupervised object discovery performance to a SlotAttention model on two datasets, and manages to disentangle objects in a third dataset where SlotAttention fails - all while being 7-70 times faster to train.
  arXiv  Detail & Related papers  (2022-04-05T09:25:28Z)
• Generalization and Robustness Implications in Object-Centric Learning [23.021791024676986]
  In this paper, we train state-of-the-art unsupervised models on five common multi-object datasets. From our experimental study, we find object-centric representations to be generally useful for downstream tasks.
  arXiv  Detail & Related papers  (2021-07-01T17:51:11Z)
• On Equivariant and Invariant Learning of Object Landmark Representations [36.214069685880986]
  We develop a simple and effective approach by combining instance-discriminative and spatially-discriminative contrastive learning. We show that when a deep network is trained to be invariant to geometric and photometric transformations, representations emerge from its intermediate layers that are highly predictive of object landmarks.
  arXiv  Detail & Related papers  (2020-06-26T04:06:56Z)
• A Critical View of the Structural Causal Model [89.43277111586258]
  We show that one can identify the cause and the effect without considering their interaction at all. We propose a new adversarial training method that mimics the disentangled structure of the causal model. Our multidimensional method outperforms the literature methods on both synthetic and real world datasets.
  arXiv  Detail & Related papers  (2020-02-23T22:52:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.