  Deep learning has shown promising results on many machine learning tasks but
DL models are often complex networks with large number of neurons and layers,
and recently, complex layer structures known as building blocks. Finding the
best deep model requires a combination of finding both the right architecture
and the correct set of parameters appropriate for that architecture. In
addition, this complexity (in terms of layer types, number of neurons, and
number of layers) also present problems with generalization since larger
networks are easier to overfit to the data. In this paper, we propose a search
framework for finding effective architectural building blocks for convolutional
neural networks (CNN). Our approach is much faster at finding models that are
close to state-of-the-art in performance. In addition, the models discovered by
our approach are also smaller than models discovered by similar techniques. We
achieve these twin advantages by designing our search space in such a way that
it searches over a reduced set of state-of-the-art building blocks for CNNs
including residual block, inception block, inception-residual block, ResNeXt
block and many others. We apply this technique to generate models for multiple
image datasets and show that these models achieve performance comparable to
state-of-the-art (and even surpassing the state-of-the-art in one case). We
also show that learned models are transferable between datasets.
