  Convolutional neural networks are becoming standard tools for solving object
recognition and visual tasks. However, most of the design and implementation of
these complex models are based on trail-and-error. In this report, the main
focus is to consider some of the important factors in designing convolutional
networks to perform better. Specifically, classification with wide single-layer
networks with large kernels as a general framework is considered. Particularly,
we will show that pre-training using unsupervised schemes is vital, reasonable
regularization is beneficial and applying of strong regularizers like dropout
could be devastating. Pool size is also could be as important as learning
procedure itself. In addition, it has been presented that using such a simple
and relatively fast model for classifying cats and dogs, performance is close
to state-of-the-art achievable by a combination of SVM models on color and
texture features.
