  Most of today's popular deep architectures are hand-engineered to be
generalists. However, this design procedure usually leads to massive redundant,
useless, or even harmful features for specific tasks. Unnecessarily high
complexities render deep nets impractical for many real-world applications,
especially those without powerful GPU support. In this paper, we attempt to
derive task-dependent compact models from a deep discriminant analysis
perspective. We propose an iterative and proactive approach for classification
tasks which alternates between (1) a pushing step, with an objective to
simultaneously maximize class separation, penalize co-variances, and push deep
discriminants into alignment with a compact set of neurons, and (2) a pruning
step, which discards less useful or even interfering neurons. Deconvolution is
adopted to reverse 'unimportant' filters' effects and recover useful
contributing sources. A simple network growing strategy based on the basic
Inception module is proposed for challenging tasks requiring larger capacity
than what the base net can offer. Experiments on the MNIST, CIFAR10, and
ImageNet datasets demonstrate our approach's efficacy. On ImageNet, by pushing
and pruning our grown Inception-88 model, we achieve more accurate models than
Inception nets generated during growing, residual nets, and popular compact
nets at similar sizes. We also show that our grown Inception nets (without
hard-coded dimension alignment) clearly outperform residual nets of similar
complexities.
