Image Completion on CIFAR-10

Code

• github.com/mswoff/Image-Completion-on-CIFAR-10
  OfficialIn papertf★ 0

Abstract

This project performed image completion on CIFAR-10, a dataset of 60,000 32x32 RGB images, using three different neural network architectures: fully convolutional networks, convolutional networks with fully connected layers, and encoder-decoder convolutional networks. The highest performing model was a deep fully convolutional network, which was able to achieve a mean squared error of .015 when comparing the original image pixel values with the predicted pixel values. As well, this network was able to output in-painted images which appeared real to the human eye.

Tasks

Reproductions