Title: This Looks Like That: Deep Learning for Interpretable Image Recognition,
Abstract: When we are faced with challenging image classification tasks, we often
explain our reasoning by dissecting the image, and pointing out prototypical
aspects of one class or another. The mounting evidence for each of the classes
helps us make our final decision. In this work, we introduce a deep network
architecture that reasons in a similar way: the network dissects the image by
finding prototypical parts, and combines evidence from the prototypes to make a
final classification. The model thus reasons in a way that is qualitatively
similar to the way ornithologists, physicians, geologists, architects, and
others would explain to people on how to solve challenging image classification
tasks. The network uses only image-level labels for training, meaning that
there are no labels for parts of images. We demonstrate our method on the
CUB-200-2011 dataset and the CBIS-DDSM dataset. Our experiments show that our
interpretable network can achieve comparable accuracy with its analogous
standard non-interpretable counterpart as well as other interpretable deep
models.