Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning

• URL: http://arxiv.org/abs/2001.01121v1
• Date: Sat, 4 Jan 2020 20:07:36 GMT
• Title: Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning
• Authors: Takashi Shinozaki
• Abstract summary: I propose to introduce unsupervised competitive learning which only requires forward propagating signals as a pre-training method for CNNs. The proposed method could be useful for a variety of poorly labeled data, for example, time series or medical data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 most powerful method in recent machine learning regimes. However, it requires large labeled data for training, and this requirement can occasionally become a barrier for real world applications. To address this problem and utilize unlabeled data, I propose to introduce unsupervised competitive learning which only requires forward propagating signals as a pre-training method for CNNs. The method was evaluated by image discrimination tasks using MNIST, CIFAR-10, and ImageNet datasets, and it achieved a state-of-the-art performance as a biologically-motivated method in the ImageNet experiment. The results suggested that the method enables higher-level learning representations solely from forward propagating signals without a backward error signal for the learning of convolutional layers. The proposed method could be useful for a variety of poorly labeled data, for example, time series or medical data.

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