1 code implementation • 1 Sep 2022 • Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi
We exemplify the possibility to overcome the limitations of the MNLM-based RC models by enriching text with the required knowledge from an external commonsense knowledge repository in controlled experiments.
1 code implementation • 17 Jun 2022 • Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi
In this work, we propose a new evaluation metric, called `rarity score', to measure the individual rarity of each image synthesized by generative models.
no code implementations • 16 Dec 2021 • Haedong Jeong, Jiyeon Han, Jaesik Choi
Despite significant improvements on the image generation performance of Generative Adversarial Networks (GANs), generations with low visual fidelity still have been observed.
no code implementations • CVPR 2021 • Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi
Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme.
no code implementations • 8 Nov 2019 • Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi
From the test, we observed that MNLMs partially understand various types of common sense knowledge but do not accurately understand the semantic meaning of relations.
no code implementations • 30 May 2019 • Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi
We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD.