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 goes over data augmentation techniques that give 20% relative improvement over last year's winner of ILSVRC.

First set of improvements is to augment training data with more kinds of distortions -- 2x zoom, different cropping approach. The second set of improvements is to combine predictions on multiple transformed versions of the image.

Because there are 90 transformed versions of the image, computing all 90 is too expensive, so they do a greedy approach where they combine predictions until there is 'no additional improvement.'

The combination approach is missing important details. How are predictions combined? What does 'no additional improvement' mean in regards to greedy method? This greedy method works on individual examples, but without knowing true labels, what's an 'improvement'?  Graph in Figure 3 shows that greedy method slightly outperforms the method which uses 'full 90 predictions', which seems counter-intuitive, but then I can't tell if it's an error because precise description of combining method is missing

Overall this paper is slightly interesting because it measures the impact of additional transformations, but hardly novel since these approaches were tried before. Also, it is interesting because it shows that ImageNet is going the way of 'MNIST research', where authors focus on incremental improvements tailored for a specific dataset (in this case -- better cropping) and might not transfer to other datasets