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 »
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 »
Understanding Failure Modes of GAN Training The idea of two competing neural networks is no doubt interesting; where, at each step one of them attempts to defeat the other one and in the process, both networks keep getting better at their job. But building such a dynamic training system is not always feasible. Generative Adversarial Networks, or GANs,… Read More »
Generative Learning refers to a special class of statistical models that are capable ofgenerating content that is very hard to distinguish from the reality (or fake content thatlooks real). The generated content could be poems, images, music, songs, videos, 3Dobjects or content from some other domain we could imagine. A domain is nothing but a fancy word for… Read More »