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pro vyhledávání: '"Kynkäänniemi, Tuomas"'
Autor:
Karras, Tero, Aittala, Miika, Kynkäänniemi, Tuomas, Lehtinen, Jaakko, Aila, Timo, Laine, Samuli
The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition, e.g., a class label or a text prompt. The popular classifier-free guid
Externí odkaz:
http://arxiv.org/abs/2406.02507
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models
Autor:
Kynkäänniemi, Tuomas, Aittala, Miika, Karras, Tero, Laine, Samuli, Aila, Timo, Lehtinen, Jaakko
Guidance is a crucial technique for extracting the best performance out of image-generating diffusion models. Traditionally, a constant guidance weight has been applied throughout the sampling chain of an image. We show that guidance is clearly harmf
Externí odkaz:
http://arxiv.org/abs/2404.07724
Autor:
Härkönen, Erik, Aittala, Miika, Kynkäänniemi, Tuomas, Laine, Samuli, Aila, Timo, Lehtinen, Jaakko
Time-lapse image sequences offer visually compelling insights into dynamic processes that are too slow to observe in real time. However, playing a long time-lapse sequence back as a video often results in distracting flicker due to random effects, su
Externí odkaz:
http://arxiv.org/abs/2207.01413
Fr\'echet Inception Distance (FID) is the primary metric for ranking models in data-driven generative modeling. While remarkably successful, the metric is known to sometimes disagree with human judgement. We investigate a root cause of these discrepa
Externí odkaz:
http://arxiv.org/abs/2203.06026
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. We present an evaluation metric that can separately and reliably measure both of these
Externí odkaz:
http://arxiv.org/abs/1904.06991
Autor:
Loppi, Niki, Kynkäänniemi, Tuomas
Publisher Copyright: © 2021, Springer Nature Switzerland AG. Copyright: Copyright 2021 Elsevier B.V., All rights reserved. StyleGAN2 is a Tensorflow-based Generative Adversarial Network (GAN) framework that represents the state-of-the-art in generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______661::7485664b211c83157fd7058fb36b5187
https://aaltodoc.aalto.fi/handle/123456789/110931
https://aaltodoc.aalto.fi/handle/123456789/110931
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