Search Results for author: Timo Aila

Found 23 papers, 18 papers with code

StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

1 code implementation23 Jan 2023 Axel Sauer, Tero Karras, Samuli Laine, Andreas Geiger, Timo Aila

Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models.

Pretrained Language Models Text-to-Image Generation

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.

Generating Long Videos of Dynamic Scenes

no code implementations7 Jun 2022 Tim Brooks, Janne Hellsten, Miika Aittala, Ting-Chun Wang, Timo Aila, Jaakko Lehtinen, Ming-Yu Liu, Alexei A. Efros, Tero Karras

Existing video generation methods often fail to produce new content as a function of time while maintaining consistencies expected in real environments, such as plausible dynamics and object persistence.

Video Generation

Elucidating the Design Space of Diffusion-Based Generative Models

4 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

The Role of ImageNet Classes in Fréchet Inception Distance

1 code implementation11 Mar 2022 Tuomas Kynkäänniemi, Tero Karras, Miika Aittala, Timo Aila, Jaakko Lehtinen

Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling.

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

118 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.

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

Few-Shot Unsupervised Image-to-Image Translation

9 code implementations ICCV 2019 Ming-Yu Liu, Xun Huang, Arun Mallya, Tero Karras, Timo Aila, Jaakko Lehtinen, Jan Kautz

Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images.

Translation Unsupervised Image-To-Image Translation

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

Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion

no code implementations SIGGRAPH 2017 Tero Karras, Timo Aila, Samuli Laine, Antti Herva, Jaakko Lehtinen

Our deep neural network learns a mapping from input waveforms to the 3D vertex coordinates of a face model, and simultaneously discovers a compact, latent code that disambiguates the variations in facial expression that cannot be explained by the audio alone.

Face Model

Pruning Convolutional Neural Networks for Resource Efficient Inference

9 code implementations19 Nov 2016 Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, Jan Kautz

We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters.

Transfer Learning

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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