Increasing robustness against background noise: Visual pattern recognition by a neocognitron

By Kunihiko Fukushima. 2011

27 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Urgen Schmidhuber
2. Alex Graves
3. David R Ha
4. Sergey Levine
5. Yoshua Bengio
6. Quoc V. Le
7. Faustino J. Gomez
8. Mohit Bansal

In Cognitive Neuroscience

1. J. Urgen Schmidhuber
2. Kunihiko Fukushima
3. Faustino J. Gomez
4. Jan Koutník
5. Olof Mogren

Citations in the corpus

• Deep learning in neural networks: An overview (2014, methodology): “For downsampling purposes he used Spatial Averaging (Fukushima, 1980, 2011) instead of Max-Pooling (MP, Section 5.11), currently a particularly convenient and popularWTA mechanism.”
• Artificial vision by multi-layered neural networks: Neocognitron and its advances (2013, methodology): “Under a noisy background, the neocognitron consisting of Scells with subtractive inhibition can be much more insensitive to interference by noise (Fukushima, 2011).”