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Computer Science

D-Index
49
Citations
34246
World Ranking
5734
National Ranking
38

Overview

Timo Aila is affiliated with Aalto University in Finland and conducts research primarily within the field of Computer Science. Their work spans multiple specialized subfields including Computer Vision and Pattern Recognition, Biophysics, General Health Professions, Radiology, Nuclear Medicine and Imaging, and Computer Graphics and Computer-Aided Design.

The scientific contributions by Timo Aila notably focus on topics such as Generative Adversarial Networks and Image Synthesis, Advanced Vision and Imaging, Cell Image Analysis Techniques, Computer Graphics and Visualization Techniques, 3D Shape Modeling and Analysis, Human Pose and Action Recognition, and Model Reduction and Neural Networks.

Their publication record includes papers in several key venues, indicating a research focus that integrates both theoretical and applied aspects of computer science and imaging sciences. Frequent publication venues include:

  • arXiv (Cornell University)
  • Aaltodoc (Aalto University)
  • ACM Transactions on Graphics

Timo Aila has frequently collaborated with other researchers, including:

  • Samuli Laine
  • Tero Karras
  • Miika Aittala
  • Jaakko Lehtinen
  • Janne Hellsten

Recent notable papers featuring Timo Aila's research or closely related collaborations 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)

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

  • Pruning Convolutional Neural Networks for Resource Efficient Inference

    Pavlo Molchanov;Stephen Tyree;Tero Karras;Timo Aila

  • 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

  • Few-Shot Unsupervised Image-to-Image Translation

    Ming-Yu Liu;Xun Huang;Arun Mallya;Tero Karras

  • 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

  • Improved Precision and Recall Metric for Assessing Generative Models

    Tuomas Kynkäänniemi;Tero Karras;Samuli Laine;Jaakko Lehtinen

  • Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder

    Chakravarty R. Alla Chaitanya;Anton S. Kaplanyan;Christoph Schied;Marco Salvi

  • Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning.

    Pavlo Molchanov;Stephen Tyree;Tero Karras;Timo Aila

  • 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

  • Incremental instant radiosity for real-time indirect illumination

    Samuli Laine;Hannu Saransaari;Janne Kontkanen;Jaakko Lehtinen

  • High-Quality Self-Supervised Deep Image Denoising

    Samuli Laine;Tero Karras;Jaakko Lehtinen;Timo Aila

Frequent Co-Authors

Samuli Laine
Samuli Laine Nvidia (United States)
Tero Karras
Tero Karras Nvidia (United Kingdom)
Jaakko Lehtinen
Jaakko Lehtinen Aalto University
David Luebke
David Luebke Nvidia (United States)
Jan Kautz
Jan Kautz Nvidia (United States)
Peter Shirley
Peter Shirley Nvidia (United States)
Michael Garland
Michael Garland Nvidia (United States)
Tomas Akenine-Möller
Tomas Akenine-Möller Nvidia (United States)
Graham D. Finlayson
Graham D. Finlayson University of East Anglia

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