Diffusion Models
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Related Articles from SNS
Diffusion Models Preferentially Memorize Prototypical Examples or: Why Does My Diffusion Model Love Slop?
arXiv:2605.30642v1 Announce Type: new Abstract: Generative models have a persistent limitation: their tendency to memorize training data can create legal liabilities and erode creative diversity. Understanding which samples are memorized in whole or in part, and under what conditions, therefore remains an important open problem. Here we answer the question "Are atypical or rare samples memorized first?"
Quantifying Error Propagation and Model Collapse in Diffusion Models
Announce Type: replace-cross Abstract: Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to significantly degrade performance in a wide range of tasks, often characterized by a progressive drift away from the target distribution. In this work, we theoretically analyze this phenomenon in the setting of score-based diffusion models.
Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning
Announce Type: replace Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focus on time-domain features while overlooking frequency-domain features, leading to frequency shift and degraded performance according to our observation. In this paper, we investigate the RL problem from a new perspective of the frequency domain.
Non-Identical Diffusion Models in MIMO-OFDM Channel Generation
arXiv:2509.01641v3 Announce Type: replace-cross Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency division multiplexing (OFDM) channel generation. Unlike the standard diffusion model that uses a scalar-valued time index to represent the global noise level, we extend this notion to an element-wise time indicator to capture local error variations more accurately. Non-identical diffusion...
Trees to Flows and Back: Unifying Decision Trees and Diffusion Models
Computer Science > Machine Learning [Submitted on 1 May 2026 (v1), last revised 21 May 2026 (this version, v2)] Title:Trees to Flows and Back: Unifying Decision Trees and Diffusion Models View PDFAbstract:Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work unifies the two by establishing a crisp mathematical correspondence between hierarchical decision trees and diffusion processes in...
Guidance for Low-Level Perceptual Editing in Unconditional Diffusion Models
Announce Type: new Abstract: Unconditional diffusion models offer powerful generative priors, yet steering them toward aesthetically enhanced outputs remains largely unexplored. We show that h-space patching, the dominant paradigm for training-free diffusion editing, systematically fails for global, low-level transformations required for aesthetic and perceptual refinement. We introduce a novel, generalized framework for image-editing in unconditional diffusion models without explicit training.
Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review
Announce Type: replace-cross Abstract: Hyperspectral image (HSI) analysis plays a critical role in remote sensing, agriculture, and environmental monitoring. However, traditional methods often struggle to handle the high dimensionality, spectral redundancy, and noise inherent in HSI data, limiting their accuracy and scalability. Recently, diffusion models including denoising diffusion probabilistic models and other generative frameworks based on stochastic differential equations have shown...
Self-Regulating Annealing in Heavy-Tailed Diffusion Models
arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient, its suitability for heavy-tailed datasets remains unclear. To address this, heavy-tailed diffusion models (HTDMs) extend the standard formulation by replacing the Gaussian distribution with a Student's t-distribution, thereby improving tail fidelity on heavy-tailed datasets.
An Improved Method for Personalizing Diffusion Models
Announce Type: replace Abstract: Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific objects based on diverse textual contexts.
Decoupled Residual Denoising Diffusion Models for Unified and Data Efficient Image-to-Image Translation
arXiv:2606.01048v1 Announce Type: new Abstract: We propose Decoupled Residual Denoising Diffusion models (DRDD) for unified and data-efficient image-to-image (I2I) translation. While diffusion models have advanced I2I translation in terms of quality and diversity, we uncover a previously under-explored property in diffusion models. Crucially, beyond its conventional role of manifold lifting (i.e., moving data off low-dimensional manifolds), injecting Gaussian noise facilitates domain...