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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, NC (Joint) |
2020-01-24 17:05 |
Okinawa | Miyakojima Marine Terminal | [Invited Talk]
Neocognitron: Deep Convolutional Neural Network Kunihiko Fukushima (FLSI)  NLP2019-100 |
Recently, deep convolutional neural networks (deep CNN) have become very popular in the field of visual pattern recognit... [more] | NLP2019-100 pp.79-82 |
| NC, MBE (Joint) |
2019-03-06 09:55 |
Tokyo | University of Electro Communications | A study of inner feature continuity of the VGG model Toya Teramoto, Hyaru Shouno (UEC) NC2018-88 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classifi... [more] | NC2018-88 pp.239-244 |
| NLP, NC (Joint) |
2019-01-23 15:40 |
Hokkaido | The Centennial Hall, Hokkaido Univ. | Measuring the Convolution Neural Network similarities trained with different dataset using SVCCA Toya Teramoto, Hayaru Shouno (UEC) NC2018-40 |
Deep Convolutional Neural Network (DCNN) is a successful model in the field of computer vision such like image classif... [more] | NC2018-40 pp.11-16 |
| NC, IPSJ-BIO, IBISML, IPSJ-MPS (Joint) [detail] |
2015-06-24 16:00 |
Okinawa | Okinawa Institute of Science and Technology | [Invited Talk]
Deep Convolutional Neural Network Neocognitron and its Advances Kunihiko Fukushima (FLSI) NC2015-3 IBISML2015-20 |
The neocognitron is a multi-layered convolutional network that can be trained to recognize visual patterns robustly. In ... [more] | NC2015-3 IBISML2015-20 pp.49-54(NC), pp.165-170(IBISML) |
| MBE, NC (Joint) |
2013-03-13 13:45 |
Tokyo | Tamagawa University | Three-staged Neocognitron: Optimal Thereshold and Thinning-out of Cells {Chihiro Yamamoto, Isao Hayashi (Kansai Univ.), Kunihiko Fukushima (FLSI) NC2012-143 |
The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. In the ... [more] |
NC2012-143 pp.55-60 |
| MBE, NC (Joint) |
2012-03-14 13:20 |
Tokyo | Tamagawa University | Training Multi-layered Neural Network Neocognitron Kunihiko Fukushima NC2011-128 |
This paper proposes new learning rules suited for training multi-layered neural networks and apply them to the neocognit... [more] | NC2011-128 pp.39-44 |
| NC | 2010-10-23 16:25 |
Fukuoka | Kyushu Inst. Tech. (Kitakyushu Sci. and Res. Park) | GPU Implementation of Neocognitron Takeharu Yoshizuka (KSU), Hiroyuki Miyamoto (KIT) NC2010-50 |
The neocognitron is a hierarchical multilayered neural network that is one of the best model of the ventral pathway of ... [more] |
NC2010-50 pp.47-51 |
| NC, MBE (Joint) |
2010-03-11 16:00 |
Tokyo | Tamagawa University | Neocognitron Trained by a New Competitive Learning Kunihiko Fukushima, Isao Hayashi (Kansai Univ.), Hayaru Shouno (Univ. of Electro-Comm.), Masayuki Kikuchi, Yuki Makino (Tokyo Univ. of Tech.) NC2009-155 |
The "neocognitron" is a hierarchical multilayered neural network capable of robust visual pattern recognition. It acqui... [more] | NC2009-155 pp.397-402 |
| NC, MBE (Joint) |
2010-03-11 16:25 |
Tokyo | Tamagawa University | Edge Extraction for the Neocognitron Yuki Makino, Masayuki Kikuchi (Tokyo Univ. of Technology), Kunihiko Fukushima, Isao Hayashi (Kansai Univ.), Hayaru Shouno (Univ. of Electro-Communications) NC2009-156 |
Neural network model neocognitron has an ability of robust visual pattern recognition. Feature-extracting cells, called ... [more] | NC2009-156 pp.403-406 |
| ITE-ME, ITS, IE, ITE-HI, ITE-AIT | 2007-02-22 10:00 |
Hokkaido | Hokkaido University | A Note on Improvement of Similar Image Clustering Method using Neocognitron
-- Introduction of Novel Structure for Extracting Color Features -- Takatoshi Ohara, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ) |
This paper proposes a new image clustering method. We have proposed an image clustering method using a neocognitron whi... [more] |
ITS2006-43 IE2006-228 pp.1-6 |
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