Contractive Slab and Spike Convolutional Deep Boltzmann Machine

Deep unsupervised learning for robust and effective feature extractions from high-resolution images still keeps greatly challenging. Although Deep Boltzmann Machine (DBM) has demonstrated the impressive capacity of feature extractions, there is still a lot of potential for improvement in scaling such model to full-sized images, the robustness, and the quality of the learned features. In this paper, we propose a Contractive Slab and Spike Convolutional Deep Boltzmann Machine in order to settle these issues. First, the proposed model extends convolution operation to the DBM in order to deal with real-size images. Second, we induce element-wise multiplication between real-valued slab hidden units and binary spike hidden units in order to enhance the quality of feature extraction in the receptive field. Then, we add the frobenius norm of the jacobian of the features as a regularization term to the maximum likelihood function in order to enhance the robustness of the features during training. The proposed regularization term results in a localized space contraction, which in turn obtains robust features on the hidden layer. Last, we use a new block-Contractive Slab and Spike Convolutional Restricted Boltzmann Machine in order to pretrain the proposed model. The proposed deep model shows the better capacity to extract high-level representations. The results on various visual tasks demonstrate the proposed model can achieve the improved performance over several state-of-the-art methods.