We propose a novel approach to representation learning based on object keypoints. It leverages the predictability of local image regions from spatial neighborhoods to identify salient regions that correspond to object parts, which are then converted to keypoints. Unlike prior approaches, this does not overly bias the keypoints to focus on a particular property of objects. We demonstrate the efficacy of our approach on Atari where we find that it learns keypoints corresponding to the most salient object parts and is more robust to certain visual distractors.