  Vision Transformers (ViTs) have successfully been applied to image
classification problems where large annotated datasets are available. On the
other hand, when fewer annotations are available, such as in biomedical
applications, image augmentation techniques like introducing image variations
or combinations have been proposed. However, regarding ViT patch sampling, less
has been explored outside grid-based strategies. In this work, we propose
Random Vision Transformer Tokens (RaViTT), a random patch sampling strategy
that can be incorporated into existing ViTs. We experimentally evaluated RaViTT
for image classification, comparing it with a baseline ViT and state-of-the-art
(SOTA) augmentation techniques in 4 datasets, including ImageNet-1k and
CIFAR-100. Results show that RaViTT increases the accuracy of the baseline in
all datasets and outperforms the SOTA augmentation techniques in 3 out of 4
datasets by a significant margin +1.23% to +4.32%. Interestingly, RaViTT
accuracy improvements can be achieved even with fewer tokens, thus reducing the
computational load of any ViT model for a given accuracy value.
