173 citations · 551 across the 20 of their papers we have counts for
23 papers
TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models
Pum Jun Kim, Yoojin Jang, Jisu Kim +1
We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, su…
Efficient Storage of Fine-Tuned Models via Low-Rank Approximation of Weight Residuals
Simo Ryu, Seunghyun Seo, Jaejun Yoo
In this paper, we present an efficient method for storing fine-tuned models by leveraging the low-rank properties of weight residuals. Our key observation is that weight residuals…
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
Dongyeun Lee, Jae Young Lee, Doyeon Kim +3
Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control betwee…
Can We Find Strong Lottery Tickets in Generative Models?
Sangyeop Yeo, Yoojin Jang, Jy-yong Sohn +2
Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network p…
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data
Jihye Park, Sunwoo Kim, Soohyun Kim +4
Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling mu…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +43
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world settin…
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution
Namhyuk Ahn, Jaejun Yoo, Kyung-Ah Sohn
In this paper, we tackle a fully unsupervised super-resolution problem, i.e., neither paired images nor ground truth HR images. We assume that low resolution (LR) images are relati…
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
Data augmentation is an effective way to improve the performance of deep networks. Unfortunately, current methods are mostly developed for high-level vision tasks (e.g., classifica…
Reliable Fidelity and Diversity Metrics for Generative Models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh +2
Devising indicative evaluation metrics for the image generation task remains an open problem. The most widely used metric for measuring the similarity between real and generated im…
StarGAN v2: Diverse Image Synthesis for Multiple Domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo +1
A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) s…
Neural Approximation of an Auto-Regressive Process through Confidence Guided Sampling
YoungJoon Yoo, Sanghyuk Chun, Sangdoo Yun +2
We propose a generic confidence-based approximation that can be plugged in and simplify the auto-regressive generation process with a proved convergence. We first assume that the p…
Time-Dependent Deep Image Prior for Dynamic MRI
Jaejun Yoo, Kyong Hwan Jin, Harshit Gupta +3
We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for the study…
Photorealistic Style Transfer via Wavelet Transforms
Jaejun Yoo, Youngjung Uh, Sanghyuk Chun +2
Recent style transfer models have provided promising artistic results. However, given a photograph as a reference style, existing methods are limited by spatial distortions or unre…
Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation
Sang-Woo Lee, Tong Gao, Sohee Yang +2
Answerer in Questioner's Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that…
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
Jang-Hyun Kim, Jaejun Yoo, Sanghyuk Chun +2
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.…
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Dongwook Lee, Jaejun Yoo, Sungho Tak +1
Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruc…
Deep Learning Diffuse Optical Tomography
Jaejun Yoo, Sohail Sabir, Duchang Heo +10
Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization lev…
Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network
Eunhee Kang, Jaejun Yoo, Jong Chul Ye
Model based iterative reconstruction (MBIR) algorithms for low-dose X-ray CT are computationally expensive. To address this problem, we recently proposed a deep convolutional neura…
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
Yo Seob Han, Jaejun Yoo, Jong Chul Ye
Purpose: The radial k-space trajectory is a well-established sampling trajectory used in conjunction with magnetic resonance imaging. However, the radial k-space trajectory require…
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Yo Seob Han, Jaejun Yoo, Jong Chul Ye
Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. How…
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification
Woong Bae, Jaejun Yoo, Jong Chul Ye
The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many pat…
A Joint Sparse Recovery Framework for Accurate Reconstruction of Inclusions in Elastic Media
Jaejun Yoo, Younghoon Jung, Mikyoung Lim +2
A robust algorithm is proposed to reconstruct the spatial support and the Lamé parameters of multiple inclusions in a homogeneous background elastic material using a few measuremen…