Review: High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis. We will dive into another inpainting method which can be regarded as an improved version of Context Encoders.
Helloooooo guys! In the previous post, we have gone through the introduction to image inpainting and the first GAN-based inpainting algorithm, Context Encoders. If you have not read the previous post, I highly recommend you to have a quick look of it first! This time, we will dive into another inpainting method which can be regarded as an improved version of Context Encoders. Let’s start!
Here, I briefly recall what we have learnt in the previous post.
In this post, I’ll demonstrate the behavior of Generative Adversarial Networks (GANs) on 3D images and how it can help to generate novel 3D images.
Deep Convolutional Generative Adversarial Networks or DCGANs are the ‘image version’ of the most fundamental implementation of GANs.
Today, I would like to give a revision for deep image inpainting we have talked about so far. Also, I want to have another review of an image inpainting paper for the consolidation of knowledge of deep image inpainting.
A dummies guide to GANs that aims at art of faking! Generative adversarial networks(GANs) took the Machine Learning field by storm last year with those impressive fake human-like faces.
Deep learning on graphs: successes, challenges, and next steps. TL;DR This is the first in a series of posts where I will discuss the evolution and future trends in the field of deep learning on graphs.