Unsupervised Part Discovery

Machine learning methods designed for unsupervised part discovery aim to identify a small number of semantically consistent parts, typically around (K=8), shared among classes in a dataset. These methods visualize all discovered parts in an image through per-part saliency maps, facilitating easier interpretation. However, as this task is unsupervised, it often relies on assumptions about the discovered parts, such as their distribution and shape.