CNN variability as data augmentation: arXiv preprint

I am thrilled to announce the preprint “Uncertain but useful: Leveraging cnn variability into data augmentation“ is available on arXiv.

Description

This work explores how variability in convolutional neural network inference can be turned into a useful signal for data augmentation, with the goal of improving model robustness and generalization.

Authors

• Inés Gonzalez Pepe
• Vinuyan Sivakolunthu
• Yohan Chatelain
• Tristan Glatard