19 citations · 105 across the 26 of their papers we have counts for
26 papers
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation
Jaeheyoung Jeon, Jung Hyun Ryu, Jewoong Cho +1
This paper presents a solution to the challenges faced by contrastive learning in sequential recommendation systems. In particular, it addresses the issue of false negative, which…
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation
Jung Hyun Ryu, Jaeheyoung Jeon, Jewoong Cho +1
Along with the exponential growth of online platforms and services, recommendation systems have become essential for identifying relevant items based on user preferences. The domai…
ReSDF: Redistancing Implicit Surfaces using Neural Networks
Yesom Park, Chang hoon Song, Jooyoung Hahn +1
This paper proposes a deep-learning-based method for recovering a signed distance function (SDF) of a given hypersurface represented by an implicit level set function. Using the fl…
AnomalyBERT: Self-Supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme
Yungi Jeong, Eunseok Yang, Jung Hyun Ryu +2
Mechanical defects in real situations affect observation values and cause abnormalities in multivariate time series, such as sensor values or network data. To perceive abnormalitie…
Feature-aligned N-BEATS with Sinkhorn divergence
Joonhun Lee, Myeongho Jeon, Myungjoo Kang +1
We propose Feature-aligned N-BEATS as a domain-generalized time series forecasting model. It is a nontrivial extension of N-BEATS with doubly residual stacking principle (Oreshkin…
Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport
Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
Optimal Transport (OT) problem investigates a transport map that bridges two distributions while minimizing a given cost function. In this regard, OT between tractable prior distri…
Restoration based Generative Models
Jaemoo Choi, Yesom Park, Myungjoo Kang
Denoising diffusion models (DDMs) have recently attracted increasing attention by showing impressive synthesis quality. DDMs are built on a diffusion process that pushes data to th…
Learning PDE Solution Operator for Continuous Modeling of Time-Series
Yesom Park, Jaemoo Choi, Changyeon Yoon +2
Learning underlying dynamics from data is important and challenging in many real-world scenarios. Incorporating differential equations (DEs) to design continuous networks has drawn…
Minimal Width for Universal Property of Deep RNN
Chang hoon Song, Geonho Hwang, Jun ho Lee +1
A recurrent neural network (RNN) is a widely used deep-learning network for dealing with sequential data. Imitating a dynamical system, an infinite-width RNN can approximate any op…
Finding the global semantic representation in GAN through Frechet Mean
Jaewoong Choi, Geonho Hwang, Hyunsoo Cho +1
The ideally disentangled latent space in GAN involves the global representation of latent space with semantic attribute coordinates. In other words, considering that this disentang…
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…
Self-Knowledge Distillation via Dropout
Hyoje Lee, Yeachan Park, Hyun Seo +1
To boost the performance, deep neural networks require deeper or wider network structures that involve massive computational and memory costs. To alleviate this issue, the self-kno…
Analyzing the Latent Space of GAN through Local Dimension Estimation
Jaewoong Choi, Geonho Hwang, Hyunsoo Cho +1
The impressive success of style-based GANs (StyleGANs) in high-fidelity image synthesis has motivated research to understand the semantic properties of their latent spaces. In this…
Solving incompressible Navier--Stokes equations on irregular domains and quadtrees by monolithic approach
Hyuntae Cho, Yesom Park, Myungjoo Kang
We present a second-order monolithic method for solving incompressible Navier--Stokes equations on irregular domains with quadtree grids. A semi-collocated grid layout is adopted,…
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…
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
Jaewoong Choi, Junho Lee, Changyeon Yoon +3
The discovery of the disentanglement properties of the latent space in GANs motivated a lot of research to find the semantically meaningful directions on it. In this paper, we sugg…
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…
MCW-Net: Single Image Deraining with Multi-level Connections and Wide Regional Non-local Blocks
Yeachan Park, Myeongho Jeon, Junho Lee +1
A recent line of convolutional neural network-based works has succeeded in capturing rain streaks. However, difficulties in detailed recovery still remain. In this paper, we presen…
Discond-VAE: Disentangling Continuous Factors from the Discrete
Jaewoong Choi, Geonho Hwang, Myungjoo Kang
In the real-world data, there are common variations shared by all classes (e.g. category label) and exclusive variations of each class. We propose a variant of VAE capable of disen…
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…
Fully implicit and accurate treatment of jump conditions for two-phase incompressible Navier-Stokes equation
Hyuntae Cho, Myungjooo Kang
We present a numerical method for two-phase incompressible Navier-Stokes equation with jump discontinuity in the normal component of the stress tensor and in the material propertie…
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…
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte +87
This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the newly introduced dataset, the proposed methods and their results. The challenge is a new versi…