Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling

• URL: http://arxiv.org/abs/2502.05743v1
• Date: Sun, 09 Feb 2025 01:58:28 GMT
• Title: Understanding Representation Dynamics of Diffusion Models via Low-Dimensional Modeling
• Authors: Xiao Li, Zekai Zhang, Xiang Li, Siyi Chen, Zhihui Zhu, Peng Wang, Qing Qu,
• Abstract summary: This work addresses the question of why and when diffusion models excel at learning high-quality representations in a self-supervised manner.<n>We develop a mathematical framework based on a low-dimensional data model and posterior estimation, revealing a fundamental trade-off between generation and representation quality near the final stage of image generation.<n>Building on these insights, we propose an ensemble method that aggregates features across noise levels, significantly improving both clean performance and robustness under label noise.
• Score: 25.705179111920806
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work addresses the critical question of why and when diffusion models, despite being designed for generative tasks, can excel at learning high-quality representations in a self-supervised manner. To address this, we develop a mathematical framework based on a low-dimensional data model and posterior estimation, revealing a fundamental trade-off between generation and representation quality near the final stage of image generation. Our analysis explains the unimodal representation dynamics across noise scales, mainly driven by the interplay between data denoising and class specification. Building on these insights, we propose an ensemble method that aggregates features across noise levels, significantly improving both clean performance and robustness under label noise. Extensive experiments on both synthetic and real-world datasets validate our findings.

Related papers

• Critical Iterative Denoising: A Discrete Generative Model Applied to Graphs [52.50288418639075]
  We propose a novel framework called Iterative Denoising, which simplifies discrete diffusion and circumvents the issue by assuming conditional independence across time. Our empirical evaluations demonstrate that the proposed method significantly outperforms existing discrete diffusion baselines in graph generation tasks.
  arXiv  Detail & Related papers  (2025-03-27T15:08:58Z)
• Revealing the Implicit Noise-based Imprint of Generative Models [71.94916898756684]
  This paper presents a novel framework that leverages noise-based model-specific imprint for the detection task. By aggregating imprints from various generative models, imprints of future models can be extrapolated to expand training data. Our approach achieves state-of-the-art performance across three public benchmarks including GenImage, Synthbuster and Chameleon.
  arXiv  Detail & Related papers  (2025-03-12T12:04:53Z)
• Unsupervised Composable Representations for Audio [0.9888599167642799]
  Current generative models are able to generate high-quality artefacts but have been shown to struggle with compositional reasoning. In this paper, we focus on the problem of compositional representation learning for music data, specifically targeting the fully-unsupervised setting. We propose a framework that leverages an explicit compositional inductive bias, defined by a flexible auto-encoding objective.
  arXiv  Detail & Related papers  (2024-08-19T08:41:09Z)
• SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder [13.453138169497903]
  SeNM-VAE is a semi-supervised noise modeling method that leverages both paired and unpaired datasets to generate realistic degraded data. We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks. Our approach excels in the quality of synthetic degraded images compared to other unpaired and paired noise modeling methods.
  arXiv  Detail & Related papers  (2024-03-26T09:03:40Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Learning with Noisy Foundation Models [95.50968225050012]
  This paper is the first work to comprehensively understand and analyze the nature of noise in pre-training datasets. We propose a tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise and improve generalization.
  arXiv  Detail & Related papers  (2024-03-11T16:22:41Z)
• Scaling Rectified Flow Transformers for High-Resolution Image Synthesis [22.11487736315616]
  Rectified flow is a recent generative model formulation that connects data and noise in a straight line. We improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. We present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities.
  arXiv  Detail & Related papers  (2024-03-05T18:45:39Z)
• The Uncanny Valley: A Comprehensive Analysis of Diffusion Models [1.223779595809275]
  Diffusion Models (DMs) have made significant advances in generating high-quality images. We explore key aspects across various DM architectures, including noise schedules, samplers, and guidance. Our comparative analysis reveals that Denoising Diffusion Probabilistic Model (DDPM)-based diffusion dynamics consistently outperform Noise Conditioned Score Network (NCSN)-based ones.
  arXiv  Detail & Related papers  (2024-02-20T20:49:22Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Realistic Noise Synthesis with Diffusion Models [44.404059914652194]
  Deep denoising models require extensive real-world training data, which is challenging to acquire.<n>We propose a novel Realistic Noise Synthesis Diffusor (RNSD) method using diffusion models to address these challenges.
  arXiv  Detail & Related papers  (2023-05-23T12:56:01Z)
• Dynamic Latent Separation for Deep Learning [67.62190501599176]
  A core problem in machine learning is to learn expressive latent variables for model prediction on complex data. Here, we develop an approach that improves expressiveness, provides partial interpretation, and is not restricted to specific applications.
  arXiv  Detail & Related papers  (2022-10-07T17:56:53Z)
• Perception Prioritized Training of Diffusion Models [34.674477039333475]
  We show that restoring data corrupted with certain noise levels offers a proper pretext for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function.
  arXiv  Detail & Related papers  (2022-04-01T06:22:23Z)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

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.