- Abstract: 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 data – e.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.
- Keywords: Ensemble classification, random subspace, data sketching
- TL;DR: Inspired by saccades we describe a simple, cheap, effective way to improve deep net performance on an image labelling task.