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 »
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 »
Handwriting Recognition also termed as HTR(Handwritten Text Recognition) is a machine learning method that aims at giving the machines an ability to read human handwriting from real-world documents(images). The traditional Optical Character Recognition systems(OCR systems) are trained to understand the variations and font-styles in the machine-printed text(from documents/images) and they work really well in practice(example-Tesseract). Handwriting Recognition on… Read More »
OpenCV-AI-toolkit was first introduced nearly 20 years ago(in 1999) by Intel Research and it is getting richer and better every year since then. OpenCV was primarily written in C++ language but has bindings for Python, Java, and MATLAB that makes it easy to integrate into different ML/AI projects. You will find almost every Computer Vision(Computer Graphics based) project… Read More »
A simple approach to predict future frames in video (PUBG) data using Python Introduction : It is impossible to predict the future! ( Unless you have a time stone -:) ). But predicting the immediate future is not very hard for us (Humans). We do it in the real-life quite often — while playing a game or watching a movie one can… Read More »