  We investigate multiple techniques to improve upon the current state of the
art deep convolutional neural network based image classification pipeline. The
techiques include adding more image transformations to training data, adding
more transformations to generate additional predictions at test time and using
complementary models applied to higher resolution images. This paper summarizes
our entry in the Imagenet Large Scale Visual Recognition Challenge 2013. Our
system achieved a top 5 classification error rate of 13.55% using no external
data which is over a 20% relative improvement on the previous year's winner.
