Title: Adversarial Representation Learning for Canonical Correlation Analysis. Abstract: Canonical correlation analysis (CCA) provides a framework to map multimodality data into a maximally correlated latent space. The deep version of CCA has replaced linear maps with deep transformations to enable more flexible correlated data representations; however, this approach requires optimization over all samples for each iteration and poorly scales. Here, we present a deep, adversarial approach to CCA, adCCA, that can be efficiently solved by standard mini-batch training. We reformulate CCA under the constraint that the different modalities are embedded with identical latent distributions, derive a tractable deep CCA target, and use an adversarial framework to efficiently learn the canonical representations. A consequence of the new formation is that adCCA learns maximally correlated representations across multimodalities meanwhile preserves structure within individual modalities. Further, adCCA removes the need for feature transformation and normalization and can be directly applied to diverse modalities and feature encodings. Numerical studies show that the performance of adCCA is robust to data transformations, binary encodings, and corruptions. Together, adCCA provides a scalable approach to align data across modalities without compromising structure within each modality.