Generative Adversarial Network in anticipation of Image Inpainting

Relating to my IFT6266 course project, I detail the implementation of a Deep Convolutional Generative Adversarial Network (DCGAN), where I hope to get a strong model that understands (or at least gets close to) the distribution behind the real images. The goal as detailed in this post, is to have a competent model in order to be able to use it effectively for reconstructing images.Read More »


Conditional Inpainting – Preprocessing data to speed up training

My original implementation for extracting a batch of data for training is not the most efficient to say the least as it greatly affects training time. Below I will detail the changes I have made to preprocess the images to accelerate training time. I’d like to thank Francis Dutil for discussing his approach with me and providing his code for inspiration.Read More »