11 citations · 16 across the 6 of their papers we have counts for
7 papers
Rethinking Image Skip Connections in StyleGAN2
Seung Park, Yong-Goo Shin
Various models based on StyleGAN have gained significant traction in the field of image synthesis, attributed to their robust training stability and superior performances. Within t…
Improving GANs with a Feature Cycling Generator
Seung Park, Yong-Goo Shin
Generative adversarial networks (GANs), built with a generator and discriminator, significantly have advanced image generation. Typically, existing papers build their generators by…
A Novel Generator with Auxiliary Branch for Improving GAN Performance
Seung Park, Yong-Goo Shin
The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and th…
Generative Convolution Layer for Image Generation
Seung Park, Yong-Goo Shin
This paper introduces a novel convolution method, called generative convolution (GConv), which is simple yet effective for improving the generative adversarial network (GAN) perfor…
Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniques
Kang-wook Kim, Seung-won Park, Junhyeok Lee +1
Recent works on voice conversion (VC) focus on preserving the rhythm and the intonation as well as the linguistic content. To preserve these features from the source, we decompose…
Generating Novel Glyph without Human Data by Learning to Communicate
Seung-won Park
In this paper, we present Neural Glyph, a system that generates novel glyph without any training data. The generator and the classifier are trained to communicate via visual symbol…
Cotatron: Transcription-Guided Speech Encoder for Any-to-Many Voice Conversion without Parallel Data
Seung-won Park, Doo-young Kim, Myun-chul Joe
We propose Cotatron, a transcription-guided speech encoder for speaker-independent linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be trai…