Can Diffusion Models Disentangle? A Theoretical Perspective
This work addresses the challenge of interpretable and controllable representation learning in AI, providing theoretical insights and practical improvements for researchers and practitioners in machine learning, though it is incremental in building on existing diffusion model theory.
The paper tackles the problem of understanding whether diffusion models can learn disentangled representations by developing a theoretical framework, establishing identifiability conditions, analyzing training dynamics, and deriving sample complexity bounds, with experiments across tasks like image colorization and voice conversion showing enhanced performance through strategies like style guidance regularization.
This paper presents a novel theoretical framework for understanding how diffusion models can learn disentangled representations. Within this framework, we establish identifiability conditions for general disentangled latent variable models, analyze training dynamics, and derive sample complexity bounds for disentangled latent subspace models. To validate our theory, we conduct disentanglement experiments across diverse tasks and modalities, including subspace recovery in latent subspace Gaussian mixture models, image colorization, image denoising, and voice conversion for speech classification. Additionally, our experiments show that training strategies inspired by our theory, such as style guidance regularization, consistently enhance disentanglement performance.