Sequential Representation Learning via Static-Dynamic Conditional Disentanglement

• URL: http://arxiv.org/abs/2408.05599v1
• Date: Sat, 10 Aug 2024 17:04:39 GMT
• Title: Sequential Representation Learning via Static-Dynamic Conditional Disentanglement
• Authors: Mathieu Cyrille Simon, Pascal Frossard, Christophe De Vleeschouwer,
• Abstract summary: This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables. Experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
• Score: 58.19137637859017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables and that improves the model expressivity through additional Normalizing Flows. A formal definition of the factors is proposed. This formalism leads to the derivation of sufficient conditions for the ground truth factors to be identifiable, and to the introduction of a novel theoretically grounded disentanglement constraint that can be directly and efficiently incorporated into our new framework. The experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.

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