  One of the difficulties of training deep neural networks is caused by
improper scaling between layers. Scaling issues introduce exploding / gradient
problems, and have typically been addressed by careful scale-preserving
initialization. We investigate the value of preserving scale, or isometry,
beyond the initial weights. We propose two methods of maintaing isometry, one
exact and one stochastic. Preliminary experiments show that for both
determinant and scale-normalization effectively speeds up learning. Results
suggest that isometry is important in the beginning of learning, and
maintaining it leads to faster learning.

Neural and Evolutionary Computing;Machine Learning;Machine Learning