How Diffusion Models Learn to Factorize and Compose

• URL: http://arxiv.org/abs/2408.13256v2
• Date: Fri, 11 Oct 2024 03:29:14 GMT
• Title: How Diffusion Models Learn to Factorize and Compose
• Authors: Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete,
• Abstract summary: Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set. We investigate whether and when diffusion models learn semantically meaningful and factorized representations of composable features.
• Score: 14.161975556325796
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diffusion models are capable of generating photo-realistic images that combine elements which likely do not appear together in the training set, demonstrating the ability to \textit{compositionally generalize}. Nonetheless, the precise mechanism of compositionality and how it is acquired through training remains elusive. Inspired by cognitive neuroscientific approaches, we consider a highly reduced setting to examine whether and when diffusion models learn semantically meaningful and factorized representations of composable features. We performed extensive controlled experiments on conditional Denoising Diffusion Probabilistic Models (DDPMs) trained to generate various forms of 2D Gaussian bump images. We found that the models learn factorized but not fully continuous manifold representations for encoding continuous features of variation underlying the data. With such representations, models demonstrate superior feature compositionality but limited ability to interpolate over unseen values of a given feature. Our experimental results further demonstrate that diffusion models can attain compositionality with few compositional examples, suggesting a more efficient way to train DDPMs. Finally, we connect manifold formation in diffusion models to percolation theory in physics, offering insight into the sudden onset of factorized representation learning. Our thorough toy experiments thus contribute a deeper understanding of how diffusion models capture compositional structure in data.

Related papers

• Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering [15.326641037243006]
  diffusion models can effectively learn the image distribution and generate new samples. We provide theoretical insights into this phenomenon by leveraging key empirical observations. We show that the minimal number of samples required to learn the underlying distribution scales linearly with the intrinsic dimensions.
  arXiv  Detail & Related papers  (2024-09-04T04:14:02Z)
• Variational Distillation of Diffusion Policies into Mixture of Experts [26.315682445979302]
  This work introduces Variational Diffusion Distillation (VDD), a novel method that distills denoising diffusion policies into Mixtures of Experts (MoE) Diffusion Models are the current state-of-the-art in generative modeling due to their exceptional ability to accurately learn and represent complex, multi-modal distributions. VDD is the first method that distills pre-trained diffusion models into MoE models, and hence, combines the expressiveness of Diffusion Models with the benefits of Mixture Models.
  arXiv  Detail & Related papers  (2024-06-18T12:15:05Z)
• A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Data [55.748186000425996]
  Recent advancements show that diffusion models can generate high-quality images. We study this phenomenon in a hierarchical generative model of data. Our analysis characterises the relationship between time and scale in diffusion models.
  arXiv  Detail & Related papers  (2024-02-26T19:52:33Z)
• Training Class-Imbalanced Diffusion Model Via Overlap Optimization [55.96820607533968]
  Diffusion models trained on real-world datasets often yield inferior fidelity for tail classes. Deep generative models, including diffusion models, are biased towards classes with abundant training images. We propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
  arXiv  Detail & Related papers  (2024-02-16T16:47:21Z)
• Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models [77.83923746319498]
  We propose a framework called Diff-Instruct to instruct the training of arbitrary generative models. We show that Diff-Instruct results in state-of-the-art single-step diffusion-based models. Experiments on refining GAN models show that the Diff-Instruct can consistently improve the pre-trained generators of GAN models.
  arXiv  Detail & Related papers  (2023-05-29T04:22:57Z)
• Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors [64.24948495708337]
  We introduce a new method that brings predicted samples to the training data manifold using a pretrained unconditional diffusion model. We perform comprehensive experiments to demonstrate the effectiveness of our approach on super-resolution, colorization, turbulence removal, and image-deraining tasks.
  arXiv  Detail & Related papers  (2022-12-14T17:26:35Z)
• Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance [95.12230117950232]
  We show that a common latent space emerges from two diffusion models trained independently on related domains. Applying CycleDiffusion to text-to-image diffusion models, we show that large-scale text-to-image diffusion models can be used as zero-shot image-to-image editors.
  arXiv  Detail & Related papers  (2022-10-11T15:53:52Z)
• Compositional Visual Generation with Composable Diffusion Models [80.75258849913574]
  We propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. The proposed method can generate scenes at test time that are substantially more complex than those seen in training.
  arXiv  Detail & Related papers  (2022-06-03T17:47:04Z)

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.