  Today's high performance deep artificial neural networks (ANNs) rely heavily
on parameter optimization, which is sequential in nature and even with a
powerful GPU, would have taken weeks to train them up for solving challenging
tasks [22]. HMAX [17] has demonstrated that a simple high performing network
could be obtained without heavy optimization. In this paper, we had improved on
the existing best HMAX neural network [12] in terms of structural simplicity
and performance. Our design replaces the L1 minimization sparse coding (SC)
with a locality-constrained linear coding (LLC) [20] which has a lower
computational demand. We also put the simple orientation filter bank back into
the front layer of the network replacing PCA. Our system's performance has
improved over the existing architecture and reached 79.0% on the challenging
Caltech-101 [7] dataset, which is state-of-the-art for ANNs (without transfer
learning). From our empirical data, the main contributors to our system's
performance include an introduction of partial signal whitening, a spot
detector, and a spatial pyramid matching (SPM) [14] layer.
