Search Results for author: Samuli Laine

Found 16 papers, 14 papers with code

Disentangling Random and Cyclic Effects in Time-Lapse Sequences

1 code implementation4 Jul 2022 Erik Härkönen, Miika Aittala, Tuomas Kynkäänniemi, Samuli Laine, Timo Aila, Jaakko Lehtinen

We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal.

Elucidating the Design Space of Diffusion-Based Generative Models

2 code implementations1 Jun 2022 Tero Karras, Miika Aittala, Timo Aila, Samuli Laine

We argue that the theory and practice of diffusion-based generative models are currently unnecessarily convoluted and seek to remedy the situation by presenting a design space that clearly separates the concrete design choices.

Image Generation

Alias-Free Generative Adversarial Networks

7 code implementations NeurIPS 2021 Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, Timo Aila

We observe that despite their hierarchical convolutional nature, the synthesis process of typical generative adversarial networks depends on absolute pixel coordinates in an unhealthy manner.

Image Generation

Modular Primitives for High-Performance Differentiable Rendering

1 code implementation6 Nov 2020 Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila

We present a modular differentiable renderer design that yields performance superior to previous methods by leveraging existing, highly optimized hardware graphics pipelines.

Analyzing and Improving the Image Quality of StyleGAN

117 code implementations CVPR 2020 Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

 Ranked #1 on Image Generation on FFHQ 1024 x 1024 (FID metric)

Conditional Image Generation

Semi-supervised semantic segmentation needs strong, high-dimensional perturbations

no code implementations25 Sep 2019 Geoff French, Timo Aila, Samuli Laine, Michal Mackiewicz, Graham Finlayson

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems.

Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation needs strong, varied perturbations

3 code implementations5 Jun 2019 Geoff French, Samuli Laine, Timo Aila, Michal Mackiewicz, Graham Finlayson

We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.

General Classification Semi-Supervised Semantic Segmentation

Improved Precision and Recall Metric for Assessing Generative Models

4 code implementations NeurIPS 2019 Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, Timo Aila

The ability to automatically estimate the quality and coverage of the samples produced by a generative model is a vital requirement for driving algorithm research.

Image Generation

Improved Self-Supervised Deep Image Denoising

no code implementations ICLR Workshop LLD 2019 Samuli Laine, Jaakko Lehtinen, Timo Aila

We describe techniques for training high-quality image denoising models that require only single instances of corrupted images as training data.

Image Denoising

High-Quality Self-Supervised Deep Image Denoising

2 code implementations NeurIPS 2019 Samuli Laine, Tero Karras, Jaakko Lehtinen, Timo Aila

We describe a novel method for training high-quality image denoising models based on unorganized collections of corrupted images.

Image Denoising

A Style-Based Generator Architecture for Generative Adversarial Networks

81 code implementations CVPR 2019 Tero Karras, Samuli Laine, Timo Aila

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

Disentanglement Image Generation

Noise2Noise: Learning Image Restoration without Clean Data

20 code implementations ICML 2018 Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, Timo Aila

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

BIG-bench Machine Learning Image Restoration +1

Temporal Ensembling for Semi-Supervised Learning

6 code implementations7 Oct 2016 Samuli Laine, Timo Aila

In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled.

General Classification Semi-Supervised Image Classification

Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks

1 code implementation21 Sep 2016 Samuli Laine, Tero Karras, Timo Aila, Antti Herva, Shunsuke Saito, Ronald Yu, Hao Li, Jaakko Lehtinen

We present a real-time deep learning framework for video-based facial performance capture -- the dense 3D tracking of an actor's face given a monocular video.

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