70 citations · 149 across the 8 of their papers we have counts for
8 papers
GC-TTS: Few-shot Speaker Adaptation with Geometric Constraints
Ji-Hoon Kim, Sang-Hoon Lee, Ji-Hyun Lee +2
Few-shot speaker adaptation is a specific Text-to-Speech (TTS) system that aims to reproduce a novel speaker's voice with a few training data. While numerous attempts have been mad…
Weakly Supervised Thoracic Disease Localization via Disease Masks
Hyun-Woo Kim, Hong-Gyu Jung, Seong-Whan Lee
To enable a deep learning-based system to be used in the medical domain as a computer-aided diagnosis system, it is essential to not only classify diseases but also present the loc…
Visual Question Answering based on Local-Scene-Aware Referring Expression Generation
Jung-Jun Kim, Dong-Gyu Lee, Jialin Wu +2
Visual question answering requires a deep understanding of both images and natural language. However, most methods mainly focus on visual concept; such as the relationships between…
Few-Shot Object Detection via Knowledge Transfer
Geonuk Kim, Hong-Gyu Jung, Seong-Whan Lee
Conventional methods for object detection usually require substantial amounts of training data and annotated bounding boxes. If there are only a few training data and annotations,…
Counterfactual Explanation Based on Gradual Construction for Deep Networks
Hong-Gyu Jung, Sin-Han Kang, Hee-Dong Kim +2
To understand the black-box characteristics of deep networks, counterfactual explanation that deduces not only the important features of an input space but also how those features…
Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
Jin-Woo Seo, Hong-Gyu Jung, Seong-Whan Lee
Few-shot learning aims to classify unseen classes with a few training examples. While recent works have shown that standard mini-batch training with a carefully designed training s…
Few-Shot Learning with Geometric Constraints
Hong-Gyu Jung, Seong-Whan Lee
In this article, we consider the problem of few-shot learning for classification. We assume a network trained for base categories with a large number of training examples, and we a…
Interpreting Undesirable Pixels for Image Classification on Black-Box Models
Sin-Han Kang, Hong-Gyu Jung, Seong-Whan Lee
In an effort to interpret black-box models, researches for developing explanation methods have proceeded in recent years. Most studies have tried to identify input pixels that are…