Conference item
Deterministic binary filters for convolutional neural networks
- Abstract:
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We propose Deterministic Binary Filters, an approach to Convolutional Neural Networks that learns weighting coefficients of predefined orthogonal binary basis instead of the conventional approach of learning directly the convolutional filters. This approach results in model architectures with significantly fewer parameters (4x to 16x) and smaller model sizes (32x due to the use of binary rather than floating point precision). We show our deterministic filter design can be integrated into well...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, pdf, 509.8KB)
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- Publisher copy:
- 10.24963/ijcai.2018/380
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Authors
Bibliographic Details
- Publisher:
- International Joint Conferences on Artificial Intelligence Organization Publisher's website
- Journal:
- IJCAI International Joint Conference on Artificial Intelligence
- Pages:
- 2739-2747
- Host title:
- Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
- Publication date:
- 2018-07-19
- Acceptance date:
- 2018-04-16
- DOI:
- ISSN:
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1045-0823
- Source identifiers:
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930482
- ISBN:
- 9780999241127
Item Description
- Keywords:
- Pubs id:
-
pubs:930482
- UUID:
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uuid:434cdb31-4fb2-4b6b-8cd6-9139ff067a5c
- Local pid:
- pubs:930482
- Deposit date:
- 2019-02-26
Terms of use
- Copyright date:
- 2018
- Notes:
- © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
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