Discriminative Deep Belief Networks for image classification
Shusen Zhou, Qingcai Chen, Xiaolong Wang · 2010
This paper presents a novel semi-supervised learning algorithm called Discriminative Deep Belief Networks (DDBN), to address the image classification problem with limited labeled data. We first construct a new deep architecture for classification using a set of Restricted Boltzmann Machines (RBM). The parameter space of the deep architecture is initially determined using labeled data together with abundant of unlabeled data, by greedy layer-wise unsupervised learning. Then, we fine-tune the whole deep networks using an exponential loss function to maximize the separability of the labeled data, by gradient-descent based supervised learning. Experiments on the artificial dataset and real image datasets show that DDBN outperforms most semi-supervised algorithm and deep learning techniques, especially for the hard classification tasks.