3 citations · 4 across the 5 of their papers we have counts for
6 papers
CREward: A Type-Specific Creativity Reward Model
Jiyeon Han, Ali Mahdavi-Amiri, Hao Zhang +1
Creativity is a complex phenomenon. When it comes to representing and assessing creativity, treating it as a single undifferentiated quantity would appear naive and underwhelming.…
Zero-Shot Document Understanding using Pseudo Table of Contents-Guided Retrieval-Augmented Generation
Hyeon Seong Jeong, Sangwoo Jo, Byeong Hyun Yoon +3
Understanding complex multimodal documents remains challenging due to their structural inconsistencies and limited training data availability. We introduce \textit{DocsRay}, a trai…
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Networks
Seongjin Park, Haedong Jeong, Tair Djanibekov +3
In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase. Along with the development of DNNs…
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks
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. As wi…
Automatic Correction of Internal Units in Generative Neural Networks
Ali Tousi, Haedong Jeong, Jiyeon Han +2
Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even…
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks
Giyoung Jeon, Haedong Jeong, Jaesik Choi
Deep generative neural networks (DGNNs) have achieved realistic and high-quality data generation. In particular, the adversarial training scheme has been applied to many DGNNs and…