Data Augmentations for n-Dimensional Image Input to CNNs

One of the greatest limiting factors for training effective deep learning frameworks is the availability, quality and organisation of the training data. To be good at classification tasks, we need to show our CNNs etc. as many examples as we possibly can. However, this is not always possible especially in situations where the training data is hard to collect e.g. medical image data. In this post, we will learn how to apply data augmentation strategies to n-Dimensional images get the most of our limited number of examples.

Generative Adversarial Network (GAN) in TensorFlow - Part 2

This tutorial will provide the data that we will use when training our Generative Adversarial Networks. It will also take an overview on the structure of the necessary code for creating a GAN and provide some skeleton code which we can work on in the next post. If you’re not up to speed on GANs, please do read the brief introduction in Part 1 of this series on Generative Adversarial Networks.

Generative Adversarial Network (GAN) in TensorFlow - Part 1

We’ve seen that CNNs can learn the content of an image for classification purposes, but what else can they do? This tutorial will look at the Generative Adversarial Network (GAN) which is able to learn from a set of images and create an entirely new ‘fake’ image which isn’t in the training set. Why? By the end of this tutorial you’ll get know why this might be done and how to do it.