title: Noise tolerance in a Neocognitron-like network.
abstract: The Neocognitron and its related hierarchical models have been shown to be competitive in recognizing handwritten digits and objects. However, the tolerance of these models to several types of noise can be low. We will start by briefly overviewing some previous results regarding the tolerance of these models. Afterwards, we report the higher noise tolerance of the winner-take-all response in a hierarchical model over related models. We provide an analysis and interpretation of this tolerance under Bayesian decision theory. Finally, we report on how to further improve recognition for extremely noisy patterns.
authors.affiliation: INESC-ID, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Av. Prof. Dr. Aníbal Cavaco Silva, 2744-016 Porto Salvo, Portugal. Electronic address: angelo.cardoso@ist.utl.pt.
authors.forename: Ângelo
authors.initials: Â
authors.lastname: Cardoso
authors.forename: Andreas
authors.initials: A
authors.lastname: Wichert
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003071:Cognition; D003198:Computer Simulation; D016208:Databases, Factual; D016571:Neural Networks, Computer; D009474:Neurons; D059629:Signal-To-Noise Ratio
keywords: Hierarchical model; Lateral competition; Neocognitron; Noise tolerance; Selectivity; Winner-take-all