Deep sparse coding network for image classification

Abstract

In this paper, we introduce a novel deep model called Deep Sparse Coding Network (DeepSCNet) for Image classification. This new model includes four types of layers: the Sparse-coding layer, the Pooling layer, the Normalization layer and the Map reduction layer. The Sparse-coding laying does the general linear coding work for local patch within the receptive field. The Pooling layer and the Normalization layer do the same work as that in CNN. The Map reduction layer reduces the CPU and memory consump-tions by reducing the number of feature maps. The paper further discusses the multi-scale, multi-locality extension to the basic DeepSCNet. Compared to CNN, training DeepSCNet is relatively easier even with a training set of moderate size. Experiments show that DeepSCNet can automatically discover highly discriminative feature directly from raw image pixels.