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Stabilizing GANs with Octave Convolutions

  • In this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to splitIn this preliminary report, we present a simple but very effective technique to stabilize the training of CNN based GANs. Motivated by recently published methods using frequency decomposition of convolutions (e.g. Octave Convolutions), we propose a novel convolution scheme to stabilize the training and reduce the likelihood of a mode collapse. The basic idea of our approach is to split convolutional filters into additive high and low frequency parts, while shifting weight updates from low to high during the training. Intuitively, this method forces GANs to learn low frequency coarse image structures before descending into fine (high frequency) details. Our approach is orthogonal and complementary to existing stabilization methods and can simply plugged into any CNN based GAN architecture. First experiments on the CelebA dataset show the effectiveness of the proposed method.show moreshow less

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Metadaten
Document Type:Article (unreviewed)
Zitierlink: https://opus.hs-offenburg.de/3958
Bibliografische Angaben
Title (English):Stabilizing GANs with Octave Convolutions
Author:Ricard Durall Lopez, Franz-Josef Pfreundt, Janis KeuperStaff MemberORCiDGND
Year of Publication:2019
Publisher:arXiv e-print
Page Number:8
URL:https://onikle.com/articles/13887
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft
Tag:Machine Learning
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt
Comment:
last revised 17 Dec 2020
ArXiv Id:http://arxiv.org/abs/1905.12534