311 citations · 916 across the 129 of their papers we have counts for
6 papers · 2 filters
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…
Unpaired Image Translation via Adaptive Convolution-based Normalization
Wonwoong Cho, Kangyeol Kim, Eungyeup Kim +2
Disentangling content and style information of an image has played an important role in recent success in image translation. In this setting, how to inject given style into an inpu…
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…
What and Where to Translate: Local Mask-based Image-to-Image Translation
Wonwoong Cho, Seunghwan Choi, Junwoo Park +3
Recently, image-to-image translation has obtained significant attention. Among many, those approaches based on an exemplar image that contains the target style information has been…
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-…