Π-nets: deep polynomial neural networks
File(s)
Author(s)
Type
Conference Paper
Abstract
Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose Π-Nets, a new class of DCNNs. Π-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. Π-Nets can be implemented using special kind of skip connections and their parameters can be represented via high-order tensors. We empirically demonstrate that Π-Nets have better representation power than standard DCNNs and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, Π-Nets produce state-of-the-art results in challenging tasks, such as image generation. Lastly, our framework elucidates why recent generative models, such as StyleGAN, improve upon their predecessors, e.g., ProGAN.
Date Issued
2020-08-05
Date Acceptance
2020-06-14
Citation
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp.7323-7333
ISSN
1063-6919
Publisher
IEEE
Start Page
7323
End Page
7333
Journal / Book Title
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Copyright Statement
© 2020 IEEE. This ICCV workshop paper is the Open Access version, provided by the Computer Vision Foundation. Except for the watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000620679507060&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Source
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Subjects
Computer Science
Computer Science, Artificial Intelligence
RECOGNITION
Science & Technology
Technology
Publication Status
Published
Start Date
2020-06-14
Finish Date
2020-06-19
Coverage Spatial
Seattle, WA, USA (Virtual)