Disentangled Representation Learning via Modular Compositional Bias
whie jung, Dong Hoon Lee, Seunghoon Hong
Recent disentangled representation learning (DRL) methods heavily rely on factor-specific strategies—either learning objectives for attributes or model architectures for objects—to embed inductive biases.Such divergent approaches lead to significant overhead when novel factors of variation do not align with prior assumptions, *e.g.*, statistical independence or spatial exclusivity, or when multiple factors coexist, as practitioners must redesign architectures or objectives.To address this, we propose a *compositional bias*, a modular inductive bias decoupled from both objectives and architectures.Specifically, we implement compositional bias by formulating DRL as maximizing compositionality. Given two sets of latent representations from different images, we create a composite representation by exchanging random subsets of latents, then maximize the likelihood and compositional consistency of the resulting composite image. We derive *mixing strategies* based on the distinct compositional structures of attribute and object factors, to embed factor-specific biases in a modular way.Under this general framework, simply adjusting the mixing strategy enables disentanglement of attributes, objects and even both without modifying the objectives or architectures.Extensive experiments demonstrate that our method competitive performance in both attribute and object disentanglement, and uniquely achieves joint disentanglement of global style and objects.