Title: On the Capability of CNNs to Generalize to Unseen Category-Viewpoint Combinations. Abstract: Object recognition and viewpoint estimation lie at the heart of visual understanding. Recent works suggest that convolutional neural networks (CNNs) fail to generalize to category-viewpoint combinations not seen during training. However, it is unclear when and how such generalization may be possible. Does the number of combinations seen during training impact generalization? What architectures better enable generalization in the multi-task setting of simultaneous category and viewpoint classification? Furthermore, what are the underlying mechanisms that drive the network’s generalization? In this paper, we answer these questions by analyzing state-of-the-art CNNs trained to classify both object category and 3D viewpoint, with quantitative control over the number of category-viewpoint combinations seen during training. We also investigate the emergence of two types of specialized neurons that can explain generalization to unseen combinations—neurons selective to category and invariant to viewpoint, and vice versa. We perform experiments on MNIST extended with position or scale, the iLab dataset with vehicles at different viewpoints, and a challenging new dataset for car model recognition and viewpoint estimation that we introduce in this paper - the Biased-Cars dataset. Our results demonstrate that as the number of combinations seen during training increase, networks generalize better to unseen category-viewpoint combinations, facilitated by an increase in the selectivity and invariance of individual neurons. We find that learning category and viewpoint in separate networks compared to a shared one leads to an increase in selectivity and invariance, as separate networks are not forced to preserve information about both category and viewpoint. This enables separate networks to significantly outperform shared ones at classifying unseen category-viewpoint combinations.