title: Pseudosaccades: A simple ensemble scheme for improving classification performance of deep nets
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.
authors: Jin Sean Lim Robert John Durrant
hIndex: Robert John Durrant 13