Monthly Archives: April 2024

Understanding Adversarial Examples and Defence Mechanisms

Adversarial Examples and Defence Mechanisms Adversarial examples are inputs to Machine Learning (ML) models that are intentionally designed to fool the model. These examples are quite easy to generate and can be created by performing intentional feature perturbation on the inputs. And, as a result they can make the ML models do false predictions. In this article, we… Read More »

Best Practices for training stable GANs

Training stable GANs Generative Adversarial Networks, or GANs for short, are quite difficult to train in practice. This is due to the nature of GAN training where two networks compete with each other in a zero-sum game. This means that one model improves at the cost of degradation in the performance of the other model. This contest makes… Read More »