  Current deep learning models for classification tasks in computer vision are
trained using mini-batches. In the present article, we take advantage of the
relationships between samples in a mini-batch, using graph neural networks to
aggregate information from similar images. This helps mitigate the adverse
effects of alterations to the input images on classification performance.
Diverse experiments on image-based object and scene classification show that
this approach not only improves a classifier's performance but also increases
its robustness to image perturbations and adversarial attacks. Further, we also
show that mini-batch graph neural networks can help to alleviate the problem of
mode collapse in Generative Adversarial Networks.
