The task of labeling data for training deep neural networks is daunting and
tedious, requiring millions of labels to achieve the current state-of-the-art
results. Such reliance on large amounts of labeled data can be relaxed by
exploiting hierarchical features via unsupervised learning techniques. In this
work, we propose to train a deep convolutional network based on an enhanced
version of the k-means clustering algorithm, which reduces the number of
correlated parameters in the form of similar filters, and thus increases test
categorization accuracy. We call our algorithm convolutional k-means
clustering. We further show that learning the connection between the layers of
a deep convolutional neural network improves its ability to be trained on a
smaller amount of labeled data. Our experiments show that the proposed
algorithm outperforms other techniques that learn filters unsupervised.
Specifically, we obtained a test accuracy of 74.1% on STL-10 and a test error
of 0.5% on MNIST.

本文介绍了一种基于增强版 k-means 聚类算法的深度卷积神经网络，该算法通过无监督学习技术利用分层特征来减少相关参数的数量，从而提高了测试分类精度。作者进一步展示了学习深度卷积神经网络各层之间的连接能够提高网络在少量标记数据上的训练能力，最终在 STL-10 数据集上获得 74.1% 的测试准确率以及在 MNIST 数据集上仅有 0.5% 的测试误差。