no code implementations • ICCV 2023 • Jae-Hyeok Lee, Dae-shik Kim
To perform effective color editing, we address two issues: (1) the entanglement of the implicit representation that causes unwanted color changes in undesired areas when learning weights, and (2) the loss of multi-view consistency when fine-tuning for a single or a few views.
no code implementations • IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 • Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, Dae-shik Kim
To address this question, we propose a novel color representation learning method for low-light image enhancement.
1 code implementation • 29 Nov 2021 • Nahyun Kim, Donggon Jang, Sunhyeok Lee, Bomi Kim, Dae-shik Kim
Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale clean/noisy paired datasets.
1 code implementation • 2 Jun 2020 • Andriy Serdega, Dae-shik Kim
On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model.
1 code implementation • 28 May 2020 • Andriy Serdega, Dae-shik Kim
On top of that, the proposed framework provides a way to evaluate mutual information between latent codes and observations for a fixed VAE model.
1 code implementation • 11 Jan 2020 • Geondo Park, Chihye Han, Wonjun Yoon, Dae-shik Kim
Thus, in addition to the joint embedding space, we propose a novel multi-head self-attention network to capture various components of visual and textual data by attending to important parts in data.
1 code implementation • 22 Aug 2019 • Deokyun Kim, Minseon Kim, Gihyun Kwon, Dae-shik Kim
Face Super-Resolution (SR) is a subfield of the SR domain that specifically targets the reconstruction of face images.
Ranked #1 on Face Alignment on CelebA + AFLW Unaligned
3 code implementations • 7 Aug 2019 • Gihyun Kwon, Chihye Han, Dae-shik Kim
We also train the model to synthesize brain disorder MRI data to demonstrate the wide applicability of our model.
no code implementations • 7 May 2019 • Chihye Han, Wonjun Yoon, Gihyun Kwon, Seungkyu Nam, Dae-shik Kim
However, DNNs exhibit idiosyncrasies that suggest their visual representation and processing might be substantially different from human vision.