Comparator Networks

Understand

The objective of this work is set-based verification, e.g.

• to decide if two sets of images of a face are of the same person or not.
• The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets.
• Instead, we design a neural network architecture that can directly learn set-wise verification.