Kim Seonghyeon
Suwon-si, Gyeonggi, Korea, Republic of
Summary
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RockstarKim Seonghyeon is a machine learning engineer with 12 years of experience, currently a Member of Engineering at poolside after ML roles at NAVER, Kakao Brain, and NAVER Cloud. He specializes in generative models and PyTorch implementations, contributing core elements and training stabilizers to widely used StyleGAN2 and related repositories. His open-source work includes implementing ModulatedConv2d, path regularization, ADA, and sampling for VQ-VAE-2, reflecting deep practical expertise in GAN training and high-fidelity image synthesis. Trained with a master’s in computer and information science from Seoul National University and a bachelor’s in psychology from Yonsei, he blends technical rigor with human-centered insight. Based in the UK, he signs contributions with a laconic “no side-effects” ethos, suggesting a preference for reproducible, well-contained code. Colleagues rely on him to turn cutting-edge research into robust, production-ready training pipelines.
12 years of coding experience
Github Skills (19)
pytorch10
operation10
python10
tensorrt10
machine-learning10
stylegan10
generative-adversarial-network10
mask-rcnn10
deep-learning10
image-generation10
tensorflow10
trainings10
faster-rcnn10
computer-vision10
tensor10
Programming languages (3)
Github contributions (5)
rosinality/stylegan2-pytorch
Dec 2019 - Nov 2021
Implementation of Analyzing and Improving the Image Quality of StyleGAN (StyleGAN 2) in PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:94 commits, 10 PRs, 71 pushes in 1 year 11 months
Contributions summary:Kim implemented initial implementations of the StyleGAN2 architecture in PyTorch, including core components such as pixel normalization, upsampling, downsampling, and the ModulatedConv2d layer. They modified the training loop, introduced path regularization for the generator, and incorporated adaptive discriminator augmentation (ADA) to stabilize training. Furthermore, the user fixed several data loading and training loop issues, improving the robustness of the training process.
pytorchanalyzingstylegancyclegandeep-learning
rosinality/style-based-gan-pytorch
Jan 2019 - Aug 2021
Implementation A Style-Based Generator Architecture for Generative Adversarial Networks in PyTorch
Role in this project:ML Engineer
ML EngineerContributions:50 commits, 11 PRs, 40 pushes in 2 years 7 months
Contributions summary:Kim primarily contributed to the development and refinement of a Style-Based Generative Adversarial Network (StyleGAN) implementation in PyTorch. Their work involved modifying the model architecture, including changes to convolutional blocks, noise injection, and the integration of adaptive instance normalization. They also addressed bugs, updated the training loop, and added features such as R1 regularization and support for style mixing.
pytorchstylegandeep-learningadversarialgenerative-adversarial-network
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