Title: Curved Data Representations in Deep Learning. Abstract: The phenomenal success of deep neural networks inspire many to understand the inner mechanisms of these models. To this end, several works have been studying geometric properties such as the intrinsic dimension of latent data representations produced by the layers of the network. In this paper, we investigate the curvature of data manifolds, i.e., the deviation of the manifold from being flat in its principal directions. We find that state-of-the-art trained convolutional neural networks have a characteristic curvature profile along layers: an initial increase, followed by a long phase of a plateau, and tailed by another increase. In contrast, untrained networks exhibit qualitatively and quantitatively different curvature profiles. We also show that the curvature gap between the last two layers is strongly correlated with the performance of the network. Further, we find that the intrinsic dimension of latent data along the network layers is not necessarily indicative of curvature. Finally, we evaluate the effect of common regularizers such as weight decay and mixup on curvature, and we find that mixup-based methods flatten intermediate layers, whereas the final layers still feature high curvatures. Our results indicate that relatively flat manifolds which transform to highly-curved manifolds toward the last layers generalize well to unseen data.