Some Improvements on Deep Convolutional Neural Network Based Image Classification
Authors: Andrew Howard
ICLR 2014

Abstract: 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.


Peer review:
[Review]:
This paper details some training tricks such as transformations, combining multiple scales and different views for ILSVRC challenge which performed pretty well compared to last years winner.

While there is nothing dramatically novel in terms of deep learning model or theory, empirical performance is the invisible hand that guides deep learning and generates buzz. So it is important to publish practical tricks-of-the trade and implementation details that can benefit all researchers.