The former reduces network size from the whole by searching optimal cell structure, while the latter compresses the network locally by removing unimportant connections in networks with weight-ranking-based pruning. These two methods will separately and sequentially lighten CNN from different scales, ...
The input data is converted to a high dimensional bit-sliced format. In the post-training stage, we analyze the impact of different bit slices to the accuracy. By pruning the redundant input bit slices and shrinking the network size, we are able to build a more compact BNN. Our result ...
Also, any pseudo-partitions can be added which would mean adding one or more rows to this matrix, depending on how many partitions can be thinly sliced. For the next predicate ci+1 op lit_(i+1), a scan is performed through the matrix (that is, going down column i+1 of the matrix...
(1) ‖⋅‖Fis Frobenius norm.XXcisN×kh×kwmatrix sliced fromc-th channel of input volumesXX,c=1,2,…,ni.WWcisni×kh×kwfilter weights sliced fromc-th channel ofWW.ββis coefficient vector of lengthnifor channel selection, andβc(c-th entry ofββ) is a scalar mask toc-th chan...
The former reduces network size from the whole by searching optimal cell structure, while the latter compresses the network locally by removing unimportant connections in networks with weight-ranking-based pruning. These two methods will separately and sequentially lighten CNN from different scales, ...