no code implementations • RepL4NLP (ACL) 2022 • Changwook Jun, Hansol Jang, Myoseop Sim, Hyun Kim, Jooyoung Choi, Kyungkoo Min, Kyunghoon Bae
Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.
no code implementations • 16 Mar 2024 • Yeongtak Oh, Jonghyun Lee, Jooyoung Choi, Dahuin Jung, Uiwon Hwang, Sungroh Yoon
To address this, we propose a novel TTA method by leveraging a latent diffusion model (LDM) based image editing model and fine-tuning it with our newly introduced corruption modeling scheme.
1 code implementation • 8 Mar 2024 • Daegyu Kim, Jooyoung Choi, Chaehun Shin, Uiwon Hwang, Sungroh Yoon
Our approach aims to approximate and integrate optimal transport into the training process, significantly enhancing the ability of diffusion models to estimate the denoiser outputs accurately.
Ranked #4 on Image Generation on CIFAR-10
no code implementations • 2 Dec 2023 • Yeongtak Oh, Jooyoung Choi, Yongsung Kim, MinJun Park, Chaehun Shin, Sungroh Yoon
Recent advancements in text-to-3D generation have significantly contributed to the automation and democratization of 3D content creation.
no code implementations • 30 May 2023 • Daegyu Kim, Chaehun Shin, Jooyoung Choi, Dahuin Jung, Sungroh Yoon
Diffusion-Stego achieved a high capacity of messages (3. 0 bpp of binary messages with 98% accuracy, and 6. 0 bpp with 90% accuracy) as well as high quality (with a FID score of 2. 77 for 1. 0 bpp on the FFHQ 64$\times$64 dataset) that makes it challenging to distinguish from real images in the PNG format.
no code implementations • 25 May 2023 • Jooyoung Choi, Yunjey Choi, Yunji Kim, Junho Kim, Sungroh Yoon
Text-to-image diffusion models can generate diverse, high-fidelity images based on user-provided text prompts.
5 code implementations • CVPR 2022 • Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, Sungroh Yoon
Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.
no code implementations • 28 Mar 2022 • Changwook Jun, Hansol Jang, Myoseop Sim, Hyun Kim, Jooyoung Choi, Kyungkoo Min, Kyunghoon Bae
Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.
1 code implementation • LREC 2022 • Changwook Jun, Jooyoung Choi, Myoseop Sim, Hyun Kim, Hansol Jang, Kyungkoo Min
Subsequently, we then build a pre-trained language model based on Transformer and fine-tune the model for table question answering with these datasets.
1 code implementation • NeurIPS 2021 • Jungbeom Lee, Jooyoung Choi, Jisoo Mok, Sungroh Yoon
Weakly supervised semantic segmentation produces pixel-level localization from class labels; however, a classifier trained on such labels is likely to focus on a small discriminative region of the target object.
Ranked #19 on Weakly-Supervised Semantic Segmentation on COCO 2014 val
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
no code implementations • 2 Oct 2021 • Yonghyun Jeong, Jooyoung Choi, Sungwon Kim, Youngmin Ro, Tae-Hyun Oh, Doyeon Kim, Heonseok Ha, Sungroh Yoon
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 controllability on attribute preservation for enhanced data utility.
1 code implementation • ICCV 2021 • Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, Sungroh Yoon
In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.
2 code implementations • ICCV 2021 • Jooyoung Choi, Jungbeom Lee, Yonghyun Jeong, Sungroh Yoon
From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased.