1 citations · 1 across the 7 of their papers we have counts for
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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…
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…
Denoising Cosine Similarity: A Theory-Driven Approach for Efficient Representation Learning
Takumi Nakagawa, Yutaro Sanada, Hiroki Waida +5
Representation learning has been increasing its impact on the research and practice of machine learning, since it enables to learn representations that can apply to various downstr…
Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE
Yuhui Zhang, Yuichiro Wada, Hiroki Waida +3
We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform…