Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition

Fukushima, Kunihiko; Miyake, Sei

doi:10.1007/978-3-642-46466-9_18

Cited by 819 publications

(477 citation statements)

References 8 publications

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“…Therefore, characterizing and modeling the functions along the hierarchy, from early or intermediate stages such as LGN, V1, are necessary steps for systematic studies in higher level, more cognitive tasks of race recognition. There are several cortex-like models proposed in recent decades [73], [74], [75], [214], [215], [216], [217]. Combined with the illustration shown in Fig.…”

Section: From Computational Intelligence To Computational Neurosciencementioning

confidence: 98%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

156

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Faces convey a wealth of social signals, including race, expression, identity, age and gender, all of which have attracted increasing attention from multi-disciplinary research, such as psychology, neuroscience, computer science, to name a few. Gleaned from recent advances in computer vision, computer graphics, and machine learning, computational intelligence based racial face analysis has been particularly popular due to its significant potential and broader impacts in extensive real-world applications, such as security and defense, surveillance, human computer interface (HCI), biometric-based identification, among others. These studies raise an important question: How implicit, non-declarative racial category can be conceptually modeled and quantitatively inferred from the face? Nevertheless, race classification is challenging due to its ambiguity and complexity depending on context and criteria. To address this challenge, recently, significant efforts have been reported toward race detection and categorization in the community. This survey provides a comprehensive and critical review of the state-of-the-art advances in face-race perception, principles, algorithms, and applications. We first discuss race perception problem formulation and motivation, while highlighting the conceptual potentials of racial face processing. Next, taxonomy of feature representational models, algorithms, performance and racial databases are presented with systematic discussions within the unified learning scenario. Finally, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potentially important cross-cutting themes and research directions for the issue of learning race from face.

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Section: From Computational Intelligence To Computational Neurosciencementioning

confidence: 98%

Learning Race from Face: A Survey

Hou

2014

IEEE Trans. Pattern Anal. Mach. Intell.

156

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“…CNNs have been introduced by Fukushima in Neocognitron [9] and occasionally used for selected image analysis tasks for the next two decades [22]. The attempts of applying them to broader classes of images remained however largely unsuccessful.…”

Section: Related Workmentioning

confidence: 99%

Learning to Play Othello With Deep Neural Networks

2018

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Abstract-Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invariance and a relatively large board. In this paper, we verify whether CNN-based move predictors prove effective for Othello, a game with significantly different characteristics, including a much smaller board size and complete lack of translational invariance. We compare several CNN architectures and board encodings, augment them with state-of-theart extensions, train on an extensive database of experts' moves, and examine them with respect to move prediction accuracy and playing strength. The empirical evaluation confirms high capabilities of neural move predictors and suggests a strong correlation between prediction accuracy and playing strength. The best CNNs not only surpass all other 1-ply Othello players proposed to date but defeat (2-ply) Edax, the best open-source Othello player.

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“…A multi-tiered organization called "Cognitron" was built to extract specific patterns and features. Then, in 1980, he proposed a new neural network model called "Neocognitron" [9]. Here, the concept of convolution and pooling firstly appeared.…”

Section: Convolutional Neuron Network's Developmentmentioning

confidence: 99%

The Understanding of Convolutional Neuron Network Family

Qi¹

2017

dtcse

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Along with the development of the computing speed and Artificial Intelligence, more and more jobs have been done by computers. Convolutional Neural Network (CNN) is one of the most popular algorithms in the field of Deep Learning. It is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. CNNs have been developed for decades. In theory, Convolutional Neural Network has gradually become more mature. However, in practical applications, it is limited by various conditions. This paper will introduce the course of Convolutional Neural Network's development today, as well as the current more mature and popular architecture and related applications of it, to help readers have more macro and comprehensive understanding of Convolutional Neural Networks.

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Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition

Cited by 819 publications

References 8 publications

Learning Race from Face: A Survey

Learning Race from Face: A Survey

Learning to Play Othello With Deep Neural Networks

The Understanding of Convolutional Neuron Network Family

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