4 citations · 7 across the 4 of their papers we have counts for
4 papers
Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning
Takashi Shinozaki
This study proposes a novel biologically-motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and back propagation (BP) learning is the…
Competitive Learning Enriches Learning Representation and Accelerates the Fine-tuning of CNNs
Takashi Shinozaki
In this study, we propose the integration of competitive learning into convolutional neural networks (CNNs) to improve the representation learning and efficiency of fine-tuning. Co…
Biologically Inspired Feedforward Supervised Learning for Deep Self-Organizing Map Networks
Takashi Shinozaki
In this study, we propose a novel deep neural network and its supervised learning method that uses a feedforward supervisory signal. The method is inspired by the human visual syst…
Competitive Learning with Feedforward Supervisory Signal for Pre-trained Multilayered Networks
Takashi Shinozaki, Yasushi Naruse
We propose a novel learning method for multilayered neural networks which uses feedforward supervisory signal and associates classification of a new input with that of pre-trained…