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.

该研究提出了一种基于生物学启发的新的深度卷积神经网络学习方法，使用无监督的竞争学习来进行预训练可解决标记数据缺乏的问题，在 MNIST、CIFAR-10 和 ImageNet 数据集上获得了最先进的表现，其高层次的学习表示方法不需要反向传播错误信号来训练卷积层，可用于各种标记不足的数据。