11 citations · 11 across the 2 of their papers we have counts for
5 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…
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…
MISO: Mutual Information Loss with Stochastic Style Representations for Multimodal Image-to-Image Translation
Sanghyeon Na, Seungjoo Yoo, Jaegul Choo
Unpaired multimodal image-to-image translation is a task of translating a given image in a source domain into diverse images in the target domain, overcoming the limitation of one-…
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…