79 citations · 98 across the 10 of their papers we have counts for
10 papers
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…
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Dongyeun Lee, Jae Young Lee, Doyeon Kim +2
Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or f…
FrePGAN: Robust Deepfake Detection Using Frequency-level Perturbations
Yonghyun Jeong, Doyeon Kim, Youngmin Ro +1
Various deepfake detectors have been proposed, but challenges still exist to detect images of unknown categories or GAN models outside of the training settings. Such issues arise f…
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth
Doyeon Kim, Woonghyun Ka, Pyungwhan Ahn +3
Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neur…
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
Juyoung Yang, Pyunghwan Ahn, Doyeon Kim +2
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area. Owing to the large amount of data acquired by sensors, unsupervised learning…
A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits
Doyeon Kim, Jeonghwan Lee, Hye Won Chung
Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answe…
Self-supervised GAN Detector
Yonghyun Jeong, Doyeon Kim, Pyounggeon Kim +2
Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news.…
MToFNet: Object Anti-Spoofing with Mobile Time-of-Flight Data
Yonghyun Jeong, Doyeon Kim, Jaehyeon Lee +3
In online markets, sellers can maliciously recapture others' images on display screens to utilize as spoof images, which can be challenging to distinguish in human eyes. To prevent…
FICGAN: Facial Identity Controllable GAN for De-identification
Yonghyun Jeong, Jooyoung Choi, Sungwon Kim +5
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed…
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection
Yonghyun Jeong, Doyeon Kim, Seungjai Min +3
The advancement in numerous generative models has a two-fold effect: a simple and easy generation of realistic synthesized images, but also an increased risk of malicious abuse of…