16 citations · 22 across the 8 of their papers we have counts for
9 papers
Bounding the Rademacher Complexity of Fourier neural operators
Taeyoung Kim, Myungjoo Kang
A Fourier neural operator (FNO) is one of the physics-inspired machine learning methods. In particular, it is a neural operator. In recent times, several types of neural operators…
Robust Out-of-Distribution Detection on Deep Probabilistic Generative Models
Jaemoo Choi, Changyeon Yoon, Jeongwoo Bae +1
Out-of-distribution (OOD) detection is an important task in machine learning systems for ensuring their reliability and safety. Deep probabilistic generative models facilitate OOD…
High-Frequency aware Perceptual Image Enhancement
Hyungmin Roh, Myungjoo Kang
In this paper, we introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods that help the network extract information fro…
OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space
Sungkwon An, Jeonghoon Kim, Myungjoo Kang +2
Novelty detection using deep generative models such as autoencoder, generative adversarial networks mostly takes image reconstruction error as novelty score function. However, imag…
Blur Invariant Kernel-Adaptive Network for Single Image Blind deblurring
Sungkwon An, Hyungmin Roh, Myungjoo Kang
We present a novel, blind, single image deblurring method that utilizes information regarding blur kernels. Our model solves the deblurring problem by dividing it into two successi…
A second-order accurate semi-Lagrangian method for convection-diffusion equations with interfacial jumps
Hyuntae Cho, Yesom Park, Myungjoo Kang
In this paper, we present a second-order accurate finite-difference method for solving convectiondiffusion equations with interfacial jumps on a moving interface. The proposed meth…