We describe a simple ensemble approach that, unlike conventional ensembles, uses multiple random data sketches ('pseudosaccades') rather than multiple classifiers to improve classification performance. Using this simple, but novel, approach we obtain statistically significant improvements in classification performance on AlexNet, GoogLeNet, ResNet-50 and ResNet-152 baselines on Imagenet datae.g. of the order of 0.3% to 0.6% in Top-1 accuracy and similar improvements in Top-k accuracy -essentially nearly for free.
The paper proposes a data augmentation technique where the input image is sub-sampled by randomly sampling rows and columns without replacement, which the authors call ‘pseudosaccades’. Rather than multiple classifiers, the authors ensemble using multiple ‘pseudosaccades’ as input, with the same network.
Comments:
I think that the proposed augmentation is a neat trick. However, the inner-workings of the method are poorly presented (or not well understood). For eg. In section 3.5, while discussing the effects of the method on individual classes, the authors mention ‘different architectures do tend to be affected by the pseudosaccades differently’ and provide no further insights.
There are no experiments that compare this method with other standard data augmentation techniques. For instance, one could use a similar ensembling technique for transformations like shear, translation, rotation, etc. by randomly sampling their corresponding parameters. I would be interested in experimental results that compare the proposed ensemble with ensembles constructed using these common techniques.
Since there is no reason for this technique to be used in isolation (I found no such motivation in the paper), it would be insightful to have experimental results where this technique is combined with the aforementioned standard augmentation techniques. Will this method’s impact on the accuracy change with these other augmentations? (Ablation studies would be useful).
This is a a form of regularization and can be thought of reverse structured dropout. Also have the authors compared this with Cutout [1, 2]? Similar experiments and comparisons would be insightful.
[1] Terrance DeVries and Graham W Taylor. Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552, 2017.
[2] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. arXiv preprint arXiv:1708.04896, 2017.
In summary:
The performance improvements are incremental. The paper lacks sufficient technical contribution. Further, it does not provide comparisons with standard techniques and similar augmentation methods to demonstrate the usefulness of the method.