# Unsupervised Holistic Image Generation from Key Local Patches

## TL;DR

This paper presents an unsupervised adversarial network approach for generating realistic images from a few key local patches without geometric priors, effectively predicting part locations and appearance.

## Contribution

It introduces a novel unsupervised method that generates images from key patches, predicting spatial arrangement without requiring labeled part data.

## Key findings

- Performs well on six diverse datasets
- Generates realistic images with accurate part placement
- Operates without supervision or labeled part annotations

## Abstract

We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene. This is a challenging problem since it requires generating realistic images and predicting locations of parts at the same time. We construct adversarial networks to tackle this problem. A generator network generates a fake image as well as a mask based on the encoder-decoder framework. On the other hand, a discriminator network aims to detect fake images. The network is trained with three losses to consider spatial, appearance, and adversarial information. The spatial loss determines whether the locations of predicted parts are correct. Input patches are restored in the output image without much modification due to the appearance loss. The adversarial loss ensures output images are realistic. The proposed network is trained without supervisory signals since no labels of key parts are required. Experimental results on six datasets demonstrate that the proposed algorithm performs favorably on challenging objects and scenes.