1 citations · 1 across the 5 of their papers we have counts for
5 papers
Local Hessian Spectral Filtering for Robust Intrinsic Dimension Estimation
Genki Osada
While diffusion models enable new approaches for estimating Local Intrinsic Dimension (LID), existing methods fail in high-dimensional spaces where noise from vast normal direction…
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching
Genki Osada, Makoto Shing, Takashi Nishide
The training of score-based diffusion models (SDMs) is based on score matching. The challenge of score matching is that it includes a computationally expensive Jacobian trace. Whil…
Understanding Likelihood of Normalizing Flow and Image Complexity through the Lens of Out-of-Distribution Detection
Genki Osada, Tsubasa Takahashi, Takashi Nishide
Out-of-distribution (OOD) detection is crucial to safety-critical machine learning applications and has been extensively studied. While recent studies have predominantly focused on…
Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty
Genki Osada, Takahashi Tsubasa, Budrul Ahsan +1
The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high…
Regularization with Latent Space Virtual Adversarial Training
Genki Osada, Budrul Ahsan, Revoti Prasad Bora +1
Virtual Adversarial Training (VAT) has shown impressive results among recently developed regularization methods called consistency regularization. VAT utilizes adversarial samples,…