In recent years, the research community has approached the problem of vehicle re-identifcation (re-id) with attention-based models, specifcally focusing on regions of a vehicle containing discriminative information. These re-id methods rely on expensive key-point labels, part annotations, and additional attributes including vehicle make, model, and color. Given the large number of vehicle re-id datasets with various levels of annotations, strongly-supervised methods are unable to scale across different domains. In this paper, we present Self-supervised Attention for Vehicle Re-identifcation (SAVER), a novel approach to effectively learn vehicle-specifc discriminative features. Through extensive experimentation, we show that SAVER improves upon the state-of-the-art on challenging vehicle re-id benchmarks including Veri-776, VehicleID, Vehicle-1M and Veri-Wild. SAVER demonstrates how proper regularization techniques signifcantly constrain the vehicle re-id task and help generate robust deep features.