Using data augmentations for adversarial analysis
The core of the adversarial performance analysis method focuses on utilizing data augmentations. Data augmentation refers to the process of introducing realistic variations to existing data programmatically. Data augmentations are commonly employed during the model training process to enhance the validation performance and generalizability of deep learning models. However, we can also leverage augmentations as an evaluation method to ensure the robustness of performance under various conditions. By applying augmentations during evaluation, practitioners can obtain a more detailed and comprehensive estimation of the model’s performance when deployed in production.
Adversarial performance analysis offers two main advantages. Firstly, it assists in building a more generalizable model by enabling better model selection during validation in training and after training between multiple trained models. This is achieved through the...
