1 citations · 1 across the 2 of their papers we have counts for
4 papers
Statistical learnability of smooth boundaries via pairwise binary classification with deep ReLU networks
Hiroki Waida, Takafumi Kanamori
The topic of nonparametric estimation of smooth boundaries is extensively studied in the conventional setting where pairs of single covariate and response variable are observed. Ho…
Scaling-based Data Augmentation for Generative Models and its Theoretical Extension
Yoshitaka Koike, Takumi Nakagawa, Hiroki Waida +1
This paper studies stable learning methods for generative models that enable high-quality data generation. Noise injection is commonly used to stabilize learning. However, selectin…
Robust VAEs via Generating Process of Noise Augmented Data
Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki +5
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative…
Investigating Self-Supervised Image Denoising with Denaturation
Hiroki Waida, Kimihiro Yamazaki, Atsushi Tokuhisa +2
Self-supervised learning for image denoising problems in the presence of denaturation for noisy data is a crucial approach in machine learning. However, theoretical understanding o…