Deep neural networks have been shown to be notoriously brittle to small perturbations in their input data, whether these pertubations arise from “natural variations”, such as cropping, rotations and scaling, or even downsampling or lossy compression such as JPG compression, or “adversarial variations”, which means the inputs have been carefully modified in order to fool the model. Fortunately, several methods have been developped in order to try to increase the robustness of models to these variations, that is their ability to be insensitive to such perturbations. Data augmentation is one of those widely used methods that is easy to deploy…

Maxime Faucher

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