# Introduction to image inpainting with deep learning In this article, we are going to learn how to do “image inpainting”, i.e. fill in missing parts of images precisely using deep learning. We’ll first discuss what image inpainting really means and the possible use cases that it can cater to . Next we’ll discuss some traditional image inpainting techniques and their shortcomings. Finally, we’ll see how to train a neural network that is capable of performing image inpainting with the CIFAR10 dataset. Here is the brief outline of the article: * Introduction to image inpainting * Traditional computer vision-based approaches * Deep learning-based approaches – Vanilla Autoencoders and Partial convolutions * Future directions and ending note ## 🤠 Read the full article [here](https://www.wandb.com/articles/introduction-to-image-inpainting-with-deep-learning). ## 😼 Check out the GitHub repo [here](https://github.com/ayulockin/deepimageinpainting). #### In colaboration with [Sayak Paul](https://sayak.dev/). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://ayushthakur.gitbook.io/ayush-thakur/authoring/introduction-to-image-inpainting-with-deep-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.