  Even though convolutional neural networks (CNN) has achieved near-human
performance in various computer vision tasks, its ability to tolerate scale
variations is limited. The popular practise is making the model bigger first,
and then train it with data augmentation using extensive scale-jittering. In
this paper, we propose a scaleinvariant convolutional neural network (SiCNN), a
modeldesigned to incorporate multi-scale feature exaction and classification
into the network structure. SiCNN uses a multi-column architecture, with each
column focusing on a particular scale. Unlike previous multi-column strategies,
these columns share the same set of filter parameters by a scale transformation
among them. This design deals with scale variation without blowing up the model
size. Experimental results show that SiCNN detects features at various scales,
and the classification result exhibits strong robustness against object scale
variations.
