Paper:
Multi-view learning seeks to form better models by making use of multiple feature sets representing the same samples. Exploiting feature correlations during training can enable better models. The traditional method for computing correlations between feature sets is Canonical Correlation Analysis (CCA), which finds linear projections that maximize correlation between feature vectors, essentially computing a shared embedding for two views of data. More recently, CCA has been used for multi-view discriminative tasks; however, CCA makes no use of class labels. Recent CCA methods have started to address this weakness but are limited in that they do not simultaneously optimize the CCA projection for discrimination and the CCA projection itself, or they are linear only. We address these deficiencies by simultaneously optimizing a CCA-based and a task objective in an end-to-end manner. Together, these two objectives learn a non-linear CCA projection to a shared latent space that is highly correlated and discriminative. Our method shows a significant improvement over previous state-of-the-art (including deep supervised approaches) for cross-view classification (8.5% increase), regularization with a second view during training when only one view is available at test time (2.2-3.2%), and semi-supervised learning (15%) on real data.

Review:
Originality:
CCA is a generative model that learns a shared subspace based on  two (or multi) views of the data. Being generative, it might not have strong discriminative power for some downstream classification tasks. Previous approaches to infuse discriminative power into the shared subspace estimated by CCA are linear. So, this paper proposes to learn 1) non-linear 2) discriminative subspaces for CCA. The paper accomplishes this by simply adding a task specific term to the optimization objective of DeepCCA (Andrew et. al. 2013), which involves just adding a task-specific MLP on top and minimizing the associated loss-function. 
1). The novelty of the proposed approach is limited. It just adds an extra term (extra neural network layer) with a corresponding weighting hyperparameter to the objective function of a previous method (DeepCCA) without much motivation.
2). The experimental setup and results are sound but some of the tasks seem contrived to show the improved performance of TOCCA methods. For instance, in the cross-view MNIST classification the authors use only projection from one view at training time and use the other view at test-time. What's the motivation for this setup? Why not split the data into train and test set by splitting observations, then train on both the views at train time and test on the held-out observations at test-time? I hope I am not missing something. 
3). Similarly, for the "Regularization for Cancer Classification" task, it's assumed that only one view is available at test time. Why is that? What are the real-world examples of such setups?  
Quality:
The paper is technically sound, though it is a trivial extension of a previous method. The experimental setup is somewhat contrived to show the superiority of the proposed method. 
Clarity:
The paper is well organized and is well written in general. The supplementary material contains more results and code will be available after the review period. 
Significance:
The paper solves an important problem by infusing discriminative power into generative subspaces learned by CCA but the results are not that important in my eyes. Since the empirical setup is a little contrived it is hard to even know whether a simple two-step approach that first estimates CCA subspace and then uses those projections in a SVM or MLP would perform comparable or better if given a fair-chance to compete.