29 citations · 59 across the 6 of their papers we have counts for
9 papers
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…