Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Controllable Orthogonalization in Training DNNs   Orthogonality is widely used for training deep neural networks (DNNs) due to
its ability to maintain all singular values of the Jacobian close to 1 and
reduce redundancy in representation. This paper proposes a computationally
efficient and numerically stable orthogonalization method using Newton's
iteration (ONI), to learn a layer-wise orthogonal weight matrix in DNNs. ONI
works by iteratively stretching the singular values of a weight matrix towards
1. This property enables it to control the orthogonality of a weight matrix by
its number of iterations. We show that our method improves the performance of
image classification networks by effectively controlling the orthogonality to
provide an optimal tradeoff between optimization benefits and representational
capacity reduction. We also show that ONI stabilizes the training of generative
adversarial networks (GANs) by maintaining the Lipschitz continuity of a
network, similar to spectral normalization (SN), and further outperforms SN by
providing controllable orthogonality.

27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
19
The categories of this paper are: eess
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph