3 citations · 8 across the 13 of their papers we have counts for
4 papers · 1 filter
Residual Prior Diffusion: A Probabilistic Framework Integrating Coarse Latent Priors with Diffusion Models
Takuro Kutsuna
Diffusion models have become a central tool in deep generative modeling, but standard formulations rely on a single network and a single diffusion schedule to transform a simple pr…
An Asymptotic Equation Linking WAIC and WBIC in Singular Models
Naoki Hayashi, Takuro Kutsuna, Sawa Takamuku
In statistical learning, models are classified as regular or singular depending on whether the mapping from parameters to probability distributions is injective. Most models with h…
A Probabilistic Formulation of Offset Noise in Diffusion Models
Takuro Kutsuna
Diffusion models have become fundamental tools for modeling data distributions in machine learning. Despite their success, these models face challenges when generating data with ex…
StyleDiff: Attribute Comparison Between Unlabeled Datasets in Latent Disentangled Space
Keisuke Kawano, Takuro Kutsuna, Ryoko Tokuhisa +2
One major challenge in machine learning applications is coping with mismatches between the datasets used in the development and those obtained in real-world applications. These mis…