Title: Neural Networks Preserve Invertibility Across Iterations: A Possible Source of Implicit Data Augmentation. Abstract: Determining what kind of representations neural networks learn, and how this may relate to generalization, remains a challenging problem. Previous work has utilized a rich set of methods to invert layer representations of neural networks, i.e. given some reference activation $\Phi_0$ and a layer function $r_{\ell}$, find $x$ which minimizes $||\Phi_0 - r_{\ell}(x)||^2$ . We show that neural networks can preserve invertibility across several iterations. That is, it is possible to interpret activations produced in some later iteration in the context of the layer function of the current iteration. For convolutional and fully connected networks, the lower layers maintain such a consistent representation for several iterations, while in the higher layers invertibility holds for fewer iterations. Adding skip connections such as those found in Resnet allows even higher layers to preserve invertibility across several iterations. We believe the fact that higher layers may interpret weight changes made by lower layers as changes to the data may produce implicit data augmentation. This implicit data augmentation may eventually yield some insight into why neural networks can generalize even with so many parameters.