  We propose Deep Feature Factorization (DFF), a method capable of localizing
similar semantic concepts within an image or a set of images. We use DFF to
gain insight into a deep convolutional neural network's learned features, where
we detect hierarchical cluster structures in feature space. This is visualized
as heat maps, which highlight semantically matching regions across a set of
images, revealing what the network `perceives' as similar. DFF can also be used
to perform co-segmentation and co-localization, and we report state-of-the-art
results on these tasks.
