Distributed sparse HMAX model

HMAX is a neuroscience-inspired deep learning model, which consists of alternating S layers and C layers, mimicking the functional mechanism of the ventral visual pathway of primates. Recently, sparse coding is introduced to the framework of HMAX for learning the S layer bases, leading to semantic features of natural images. However, when processing large scale datasets, the sparse HMAX is beset with large time and memory consumption during both training and testing. In this paper, we present a distributed algorithm to parallelize the sparse coding in S layers. Experiments on a cluster with eight nodes verify that the algorithm can also learn higher level semantic representations of objects but with much higher efficiency than the original model. The acceleration rate is linearly proportional to the number of nodes, which shows the good scalability of the proposed method.