Unsupervised Part Discovery

Unsupervised part discovery is a machine learning approach aimed at identifying a small number of semantically consistent parts shared across categories in a dataset, typically around 8. This method simplifies the interpretation of results by visualizing all discovered parts in images through their saliency maps. Although unsupervised, the task relies on assumptions about the distribution and shape of parts, making it valuable for various applications.