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Deep learning has shown promising results on many machine learning tasks butDL models are often complex networks with large number of neurons and layers,and recently, complex layer structures known as building blocks. Finding thebest deep model requires a combination of finding both the right architectureand the correct set of parameters appropriate for that architecture. Inaddition, this complexity (in terms of layer types, number of neurons, andnumber of layers) also present problems with generalization since largernetworks are easier to overfit to the data. In this paper, we propose a searchframework for finding effective architectural building blocks for convolutionalneural networks (CNN). Our approach is much faster at finding models that areclose to state-of-the-art in performance. In addition, the models discovered byour approach are also smaller than models discovered by similar techniques. Weachieve these twin advantages by designing our search space in such a way thatit searches over a reduced set of state-of-the-art building blocks for CNNsincluding residual block, inception block, inception-residual block, ResNeXtblock and many others. We apply this technique to generate models for multipleimage datasets and show that these models achieve performance comparable tostate-of-the-art (and even surpassing the state-of-the-art in one case). Wealso show that learned models are transferable between datasets.

Effective Building Block Design for Deep Convolutional Neural Networks using Search