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Computer Science

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Overview

Samuli Laine is a researcher affiliated with Nvidia in the United States. Their work primarily spans the field of computer science, with a focus on computer vision and pattern recognition, computer graphics and computer-aided design, computational mechanics, biophysics, and radiology, nuclear medicine and imaging as key subfields of study.

Their research encompasses a variety of topics, notably:

  • Generative Adversarial Networks and Image Synthesis
  • Advanced Vision and Imaging
  • Computer Graphics and Visualization Techniques
  • 3D Shape Modeling and Analysis
  • Cell Image Analysis Techniques
  • Astrophysics and Cosmic Phenomena
  • Radio Astronomy Observations and Technology

Samuli Laine has contributed several publications to prominent venues. Most of their work appears in arXiv (Cornell University), accounting for nine publications, alongside contributions to ACM Transactions on Graphics and Aaltodoc (Aalto University), as well as single publications in Monthly Notices of the Royal Astronomical Society and Eurographics.

Key recent papers include:

  • "Training Generative Adversarial Networks with Limited Data" (2020, arXiv (Cornell University))
  • "Alias-Free Generative Adversarial Networks" (2021, arXiv (Cornell University))
  • "Elucidating the Design Space of Diffusion-Based Generative Models" (2022, arXiv (Cornell University))
  • "eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers" (2022, arXiv (Cornell University))
  • "StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis" (2023, arXiv (Cornell University))

Collaboration is an important aspect of their research, with frequent co-authors including Miika Aittala, Jaakko Lehtinen, Timo Aila, Tero Karras, and Janne Hellsten, demonstrating ongoing partnerships in related scientific inquiries.

Best Publications

  • A Style-Based Generator Architecture for Generative Adversarial Networks

    Tero Karras;Samuli Laine;Timo Aila

  • Analyzing and Improving the Image Quality of StyleGAN

    Tero Karras;Samuli Laine;Miika Aittala;Janne Hellsten

  • Progressive Growing of GANs for Improved Quality, Stability, and Variation

    Tero Karras;Timo Aila;Samuli Laine;Jaakko Lehtinen

  • A Style-Based Generator Architecture for Generative Adversarial Networks

    Tero Karras;Samuli Laine;Timo Aila

  • Noise2Noise: Learning image restoration without clean data

    Jaakko Lehtinen;Jaakko Lehtinen;Jacob Munkberg;Jon Hasselgren;Samuli Laine

  • Temporal Ensembling for Semi-Supervised Learning

    Samuli Laine;Timo Aila

  • Temporal ensembling for semi-supervised learning

    Samuli Matias Laine;Timo Oskari Aila

  • Training Generative Adversarial Networks with Limited Data

    Tero Karras;Miika Aittala;Janne Hellsten;Samuli Laine

  • Elucidating the Design Space of Diffusion-Based Generative Models

    Unknown

  • Alias-Free Generative Adversarial Networks

    Tero Karras;Miika Aittala;Samuli Laine;Erik Härkönen

  • Understanding the efficiency of ray traversal on GPUs

    Timo Aila;Samuli Laine

  • Audio-driven facial animation by joint end-to-end learning of pose and emotion

    Tero Karras;Timo Aila;Samuli Laine;Antti Herva

  • Efficient Sparse Voxel Octrees

    S Laine;T Karras

  • Improved Precision and Recall Metric for Assessing Generative Models

    Tuomas Kynkäänniemi;Tero Karras;Samuli Laine;Jaakko Lehtinen

  • Modular primitives for high-performance differentiable rendering

    Samuli Laine;Janne Hellsten;Tero Karras;Yeongho Seol

  • Semi-supervised semantic segmentation needs strong, varied perturbations.

    Geoffrey French;Samuli Laine;Timo Aila;Michal Mackiewicz

  • Ambient occlusion fields

    Janne Kontkanen;Samuli Laine

  • Incremental instant radiosity for real-time indirect illumination

    Samuli Laine;Hannu Saransaari;Janne Kontkanen;Jaakko Lehtinen

  • Efficient sparse voxel octrees

    Samuli Laine;Tero Karras

  • High-Quality Self-Supervised Deep Image Denoising

    Samuli Laine;Tero Karras;Jaakko Lehtinen;Timo Aila

  • Alias-free shadow maps

    Timo Aila;Samuli Laine

Frequent Co-Authors

Timo Aila
Timo Aila Aalto University
Tero Karras
Tero Karras Nvidia (United Kingdom)
Jaakko Lehtinen
Jaakko Lehtinen Aalto University
David Luebke
David Luebke Nvidia (United States)
Peter Shirley
Peter Shirley Nvidia (United States)
Michael Garland
Michael Garland Nvidia (United States)
Graham D. Finlayson
Graham D. Finlayson University of East Anglia
Alexander Keller
Alexander Keller Nvidia (United States)
Tomas Akenine-Möller
Tomas Akenine-Möller Nvidia (United States)

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