6 papers
Exploring Unlabeled Faces for Novel Attribute Discovery
Hyojin Bahng, Sunghyo Chung, Seungjoo Yoo +1
Despite remarkable success in unpaired image-to-image translation, existing systems still require a large amount of labeled images. This is a bottleneck for their real-world applic…
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Cheonbok Park, Chunggi Lee, Hyojin Bahng +5
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamic…
Learning De-biased Representations with Biased Representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun +2
Many machine learning algorithms are trained and evaluated by splitting data from a single source into training and test sets. While such focus on in-distribution learning scenario…
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung +3
Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount o…
MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation
David Keetae Park, Seungjoo Yoo, Hyojin Bahng +2
Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modali…
Coloring with Words: Guiding Image Colorization Through Text-based Palette Generation
Hyojin Bahng, Seungjoo Yoo, Wonwoong Cho +4
This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generat…