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Table 1 Notations and their definitions

From: An efficient pruning scheme of deep neural networks for Internet of Things applications

Notation Definition
L The number of convolutional layers
p The overall pruning rate for all channels
Cl The original total number of channels in
  each layer, 1≤lL
\(\mathbf {w}^{l}_{k}\) The convolutional kernel,
  \(\mathbf {w}^{l}_{k}\in \mathbb {R}^{C^{l-1}\times 3\times 3},1\le l\le L, 1\le k \le C^{l}\)
Hl The height of channels, 1≤lL
Wl The width of channels, 1≤lL
\(\mathbf {z}^{l}_{k}\) The feature map or channel,
  \(\mathbf {z}^{l}_{k}\in \mathbb {R}^{H^{l}\times W^{l}},1\le l\le L, 1\le k \le C^{l}\)
fl The feature saliency, \(\mathbf {f}^{l}\in \mathbb {R}^{C^{l}}\)
  \(f^{l}_{k}\in \mathbf {f}^{l}, 1\le k\le C^{l}\)
[al]i The remaining channels in each layer in
  the i-th training epoch, 1≤lL
\(\Theta ^{l}_{k}\) The evaluation on channels’ significance w.r.t.
  a single mini-batch, 1≤lL,1≤kCl
[ξl]i The layers’ significance evaluation in the i-th
  training epoch, 1≤lL
J The loss function adopted to evaluate the difference
  between the observed values and the actual ones
ε The smoothing factor
s The proportion of redistributing channels