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 »
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 »
Image Synthesis using Pixel CNN based Autoregressive Generative Models Recent advances in the field of deep learning have led to the development of complex generative models that are capable of generating high quality content in the form of text, audio, pictures, videos and so on. Generative models that make use of deep learning architectures to tackle the task… Read More »
What are Autoregressive Generative Models? The term ‘autoregressive’ is taken from the field of time-series forecasting frameworks. Where, In order to make a future prediction, a model considers all the past observations in a timely manner. Autoregressive generative models are also quite similar in nature. They also take help from all their past predictions in order to decide… Read More »
Building blocks of deep generative models In this article, we will learn about some important concepts that are important to learn if we want to thoroughly understand how a deep generative learning model works. We will learn about some probabilistic concepts that help the generative learning frameworks in learning data distributions. These concepts are basic building blocks of… Read More »
Generative Learning and its Differences from the Discriminative Learning 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.… Read More »
This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders ..etc), Variational Autoencoders (VAEs) are generative models like GANs (Generative Adversarial Networks). This article is primarily focused on the Variational Autoencoders and… Read More »
Autoencoders are unsupervised Deep Learning techniques that are extensively used for dimensionality reduction, latent feature learning (Learning Representations), and also as generative models (Generative Adversarial Networks: GANs). Denoising Autoencoders are slight modifications to the vanilla autoencoders that can be used for reducing noise from real-world noisy datasets. In this tutorial, we will investigate Convolutional Denoising Autoencoders to reduce… Read More »