We recently observed that convolutional filters initialized
 farthest apart from each other using offthe-
 shelf pre-computed Grassmannian subspace
 packing codebooks performed surprisingly well
 across many datasets. Through this short paper,
 we’d like to disseminate some initial results in this
 regard in the hope that we stimulate the curiosity
 of the deep-learning community towards considering
 classical Grassmannian subspace packing
 results as a source of new ideas for more efficient
 initialization strategies.
Initialize weights using off-the-shelf Grassmannian codebooks, get  faster training and better accuracy