M
Miika Aittala
Researcher at Massachusetts Institute of Technology
Publications - 45
Citations - 9276
Miika Aittala is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Rendering (computer graphics). The author has an hindex of 21, co-authored 34 publications receiving 3587 citations. Previous affiliations of Miika Aittala include Aalto University & VTT Technical Research Centre of Finland.
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Analyzing and Improving the Image Quality of StyleGAN
TL;DR: This work redesigns the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images, and thereby redefines the state of the art in unconditional image modeling.
Proceedings ArticleDOI
Analyzing and Improving the Image Quality of StyleGAN
TL;DR: In this paper, the authors propose to redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images.
Proceedings Article
Training Generative Adversarial Networks with Limited Data
TL;DR: It is demonstrated, on several datasets, that good results are now possible using only a few thousand training images, often matching StyleGAN2 results with an order of magnitude fewer images, and is expected to open up new application domains for GANs.
Posted Content
Alias-Free Generative Adversarial Networks
Tero Karras,Miika Aittala,Samuli Laine,Erik Härkönen,Janne Hellsten,Jaakko Lehtinen,Timo Aila +6 more
TL;DR: It is observed that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner, and small architectural changes are derived that guarantee that unwanted information cannot leak into the hierarchical synthesis process.
Proceedings Article
Noise2Noise: Learning image restoration without clean data
Jaakko Lehtinen,Jaakko Lehtinen,Jacob Munkberg,Jon Hasselgren,Samuli Laine,Tero Karras,Miika Aittala,Timo Aila +7 more
TL;DR: In this article, the authors apply basic statistical reasoning to signal reconstruction by machine learning, learning to map corrupted observations to clean signals without explicit image priors or likelihood models of the corruption, and show that a single model learns photographic noise removal, denoising synthetic Monte Carlo images, and reconstruction of undersampled MRI scans.