| Presentation | 2020-01-24 [Invited Talk] Neocognitron: Deep Convolutional Neural Network Kunihiko Fukushima, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Recently, deep convolutional neural networks (deep CNN) have become very popular in the field of visual pattern recognition. The neocognitron, which was first proposed by Fukushima (1979), is a network classified to this category. Its architecture was suggested by neurophysiological findings on the visual systems of mammals. It is a hierarchical multi-layered network. It acquires the ability to recognize visual patterns robustly through learning. Although the neocognitron has a long history, improvements of the network are still continuing. For example, learning rule AiS (add-if-silent) for intermediate layers, learning rule mWTA (margined WTA) for the deepest layer, pattern classification by IntVec (interpolating-vector), method for reducing the computational cost of IntVec without sacrificing the recognition rate, and so on. This paper discusses the recent neocognitron focusing on differences from the conventional deep CNN. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | neocognitron / deep CNN / visual pattern recognition / AiS (add-if-silent) / IntVec (interpolating-vector) |
| Paper # | NLP2019-100 |
| Date of Issue | 2020-01-16 (NLP) |
| Conference Information | |
| Committee | NLP / NC |
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| Conference Date | 2020/1/23(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Miyakojima Marine Terminal |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hiroaki Kurokawa(Tokyo Univ. of Tech.) / Hayaru Shouno(UEC) |
| Vice Chair | Kiyohisa Natsume(Kyushu Inst. of Tech.) / Kazuyuki Samejima(Tamagawa Univ) |
| Secretary | Kiyohisa Natsume(Nippon Inst. of Tech.) / Kazuyuki Samejima(Kyushu Inst. of Tech.) |
| Assistant | Yutaka Shimada(Saitama Univ.) / Toshikaza Samura(Yamaguchi Univ.) / Takashi Shinozaki(NICT) / Ken Takiyama(TUAT) |
| Paper Information | |
| Registration To | Technical Committee on Nonlinear Problems / Technical Committee on Neurocomputing |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | [Invited Talk] Neocognitron: Deep Convolutional Neural Network |
| Sub Title (in English) | |
| Keyword(1) | neocognitron |
| Keyword(2) | deep CNN |
| Keyword(3) | visual pattern recognition |
| Keyword(4) | AiS (add-if-silent) |
| Keyword(5) | IntVec (interpolating-vector) |
| 1st Author's Name | Kunihiko Fukushima |
| 1st Author's Affiliation | Fuzzy Logic Systems Institute(FLSI) |
| Date | 2020-01-24 |
| Paper # | NLP2019-100 |
| Volume (vol) | vol.119 |
| Number (no) | NLP-381 |
| Page | pp.pp.79-82(NLP), |
| #Pages | 4 |
| Date of Issue | 2020-01-16 (NLP) |