88 citations · 127 across the 4 of their papers we have counts for
5 papers
Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
Hiromu Yakura, Yuki Koyama, Masataka Goto
Current deep learning techniques for style transfer would not be optimal for design support since their "one-shot" transfer does not fit exploratory design processes. To overcome t…
Generative Melody Composition with Human-in-the-Loop Bayesian Optimization
Yijun Zhou, Yuki Koyama, Masataka Goto +1
Deep generative models allow even novice composers to generate various melodies by sampling latent vectors. However, finding the desired melody is challenging since the latent spac…
Sequential Gallery for Interactive Visual Design Optimization
Yuki Koyama, Issei Sato, Masataka Goto
Visual design tasks often involve tuning many design parameters. For example, color grading of a photograph involves many parameters, some of which non-expert users might be unfami…
MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images
Takayuki Nakatsuka, Kazuyoshi Yoshii, Yuki Koyama +3
This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (i…
Computational Design with Crowds
Yuki Koyama, Takeo Igarashi
Computational design is aimed at supporting or automating design processes using computational techniques. However, some classes of design tasks involve criteria that are difficult…