3 citations · 5 across the 6 of their papers we have counts for
7 papers
DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization
Dongyeun Lee, Jiwan Hur, Hyounguk Shon +2
Diffusion models have achieved remarkable success in image generation but come with significant computational costs, posing challenges for deployment in resource-constrained enviro…
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency
Woonghyun Ka, Jae Young Lee, Jaehyun Choi +1
In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth net…
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep
Jae Young Lee, Woonghyun Ka, Jaehyun Choi +1
We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learn…
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations
Jae Young Lee, Wonjun Lee, Jaehyun Choi +2
Anomaly detection is a critical and challenging task that aims to identify data points deviating from normal patterns and distributions within a dataset. Various methods have been…
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
Dong-Jae Lee, Jae Young Lee, Hyounguk Shon +4
Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera ca…
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…