A Robust Unsupervised Feature Learning Framework Using Spatial Boosting Networks

Abstract

To boost up power of unsupervised feature learning and deep learning, there has been a great effort in optimizing network structure to learn more efficient high level features. It is crucial for a network to have a sufficient amount of learnable parameters yet still be able to capture in variances in data. In this paper, the authors propose spatial boosting networks, which employ convolutional feature learning networks as learning components. Each component in a network is assigned to a certain spatial region. This allows the network learn more adaptive features for each region. In order to make spatial boosting networks to capture relationship between regions of the visual field, we also propose convolutional pooling procedure. By expanding pooling scope into overlapping regions, we expect the features pooled in higher level to be more robust to noises and more invariant to transformation. Experiments show that using spatial boosting networks boosts up accuracy up to 3% from conventional approaches in standard datasets CIFAR and STL. Moreover, these results are competitive in comparison with other methods by using only a basic feature learning algorithm.