In recent years, machine learning algorithms have been used in a wide range of applications, including image processing. Adversarial attacks have become a popular way of fooling image recognition algorithms, and various methods have been developed to generate such attacks. Adversarial Color Enhancement is a technique that exploits the color information of an image to find adversarial examples.
What is Adversarial Color Enhancement?
Adversarial Color Enhancement is a technique used to generate
Differentiable Augmentation (DiffAugment) is a special set of image transformations that are used during GAN (Generative Adversarial Network) training to modify data. The transformations are applied to the real and artificially created images. The unique thing about DiffAugment is that it allows the gradients to pass through the changes back to the generator, which helps to control training dynamics.
What is the Purpose of DiffAugment?
The goal of augmentations is to help create more diverse
Overview: MaxUp
MaxUp is a powerful technique that can be used to improve the generalization performance of machine learning models by generating a set of augmented data with random perturbations or transforms. This not only improves the model's generalization accuracy but also makes it more robust to random fluctuations in the data.
What is MaxUp?
MaxUp is an adversarial data augmentation technique that introduces a smoothness or robustness regularization against random perturbations. As a