Paper:
The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance. Yet despite significant empirical and theoretical analysis, relatively little has been proved about the concrete effects of different initialization schemes. In this work, we analyze the effect of initialization in deep linear networks, and provide for the first time a rigorous proof that drawing the initial weights from the orthogonal group speeds up convergence relative to the standard Gaussian initialization with iid weights. We show that for deep networks, the width needed for efficient convergence to a global minimum with orthogonal initializations is independent of the depth, whereas the width needed for efficient convergence with Gaussian initializations scales linearly in the depth. Our results demonstrate how the benefits of a good initialization can persist throughout learning, suggesting an explanation for the recent empirical successes found by initializing very deep non-linear networks according to the principle of dynamical isometry.

Review:
This paper studies the convergence of deep linear networks under orthogonal initialization. Most of the problem setup, analysis techniques and proof roadmap are adapted from Du & Hu (2019). The difference is the orthogonal initialization compared with the Gaussian initialization in Du & Hu (2019). Since the product of orthogonal weight matrices is identity, the new initialization can remove the dependency of the number of nodes $m$ on the depth $L$ of the network. The authors also proved a lower bound of the loss function trained by Gaussian initialization within certain iterations. This justifies the disadvantage of Gaussian initialization.
My biggest concern is that this paper seems to be very similar to Du & Hu (2019) in many places. The whole Sections 3 & 4 are almost the same as Sections 3,4,5 & 7 in their paper. The contribution in this paper is too incremental given previous work. 
What is the advantage of using orthogonal matrices in (4) compared with just using identity matrices as initialization? Can we prove the same result with just identity initialization? In this case, what would we lose by restricting us to this special case?
In the proof of Lemma 4.2, it seems that only the randomness of the last layer $W_L(0)$ is used. Why do we need all the layers to be uniformly sampled?
It should be explained in more details that $W_L(0)$ is drawn from a uniform distribution over orthogonal matrices in $d_y\times d_y$ space. Then $1/\sqrt{m} W_L(0)\cdot z /\|z\|^2$ is not distributed on the whole space of $d_y$-sphere. The argument in the proof of Lemma 4.2 thus needs more justification.
It would be interesting to see an empirical comparison of the proposed initialization and the Gaussian initialization. Due to the lower bound proved in this paper, the experiments are expected to show distinct difference between these two.