U
Uiwon Hwang
Researcher at Seoul National University
Publications - 22
Citations - 542
Uiwon Hwang is an academic researcher from Seoul National University. The author has contributed to research in topics: Autoencoder & Missing data. The author has an hindex of 10, co-authored 19 publications receiving 368 citations.
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How Generative Adversarial Networks and Their Variants Work: An Overview
TL;DR: In this paper, the authors discuss the details of GANs for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives.
Journal ArticleDOI
How Generative Adversarial Networks and Their Variants Work: An Overview
TL;DR: In this article, the authors discuss the details of GAN for those readers who are familiar with, but do not comprehend GAN deeply or who wish to view GAN from various perspectives.
Posted Content
Reinforcement Learning based Recommender System using Biclustering Technique
TL;DR: This paper forms a novel RL-based recommender system as a gridworld game by using a biclustering technique that can reduce the state and action space significantly and improves the recommendation quality effectively handling the cold-start problem.
Journal ArticleDOI
PuVAE: A Variational Autoencoder to Purify Adversarial Examples
TL;DR: The proposed Purifying Variational AutoEncoder (PuVAE), a method to purify adversarial examples, exhibits performances that are competitive with state-of-the-art defense methods, and the inference time is approximately 130 times faster than that of Defense-GAN which is a state of the art purifier method.
Posted Content
How Generative Adversarial Networks and its variants Work: An Overview of GAN
TL;DR: Details of GAN are looked into that firstly show how it operates and fundamental meaning of objective functions and point to GAN variants applied to vast amount of tasks.