# Filter Pruning via Attention Consistency on Feature Maps
## 4 Experiment and Analysis
### Results and Analysis
#### 4.2.2 Results on ImageNet
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline** & **Method** & **Top-1(\%)** & **Top-1(\%)** & **FLOPs(PR)** & **Params(PR)** \\ \hline \multirow{8}{*}{VGG-16} & FPGM [23] & 93.58 \(\rightarrow\) 93.54 & 0.04 & 206.43M (34.2\%) & - \\  & L1 [21] & 93.25 \(\rightarrow\) 93.40 & \(-\)0.15 & 206.12M (34.3\%) & 5.40M (64.0\%) \\  & VP [32] & 93.25 \(\rightarrow\) 93.18 & 0.07 & 190.00M (39.1\%) & 3.92M (73.3\%) \\  & SSS [35] & 93.96 \(\rightarrow\) 93.02 & 0.84 & 183.13M (41.6\%) & 3.93M (73.8\%) \\  & GAL-0.05 [37] & 93.96 \(\rightarrow\) 92.03 & 1.93 & 189.49M (39.6\%) & 3.36M (77.6\%) \\  & **FPAC** & 93.96 \(\rightarrow\) 94.03 & \(-\)0.07 & 131.17M (58.1\%) & 2.76M (81.6\%) \\  & GAL-0.1 [37] & 93.96 \(\rightarrow\) 90.73 & 3.23 & 171.89M (45.2\%) & 2.67M (82.2\%) \\  & HRank [31] & 93.96 \(\rightarrow\) 93.43 & 0.53 & 145.61M (53.5\%) & 2.51M (82.9\%) \\  & **FPAC** & 93.96 \(\rightarrow\) 93.86 & 0.10 & 104.78M (66.6\%) & 2.50M (83.3\%) \\ \hline \multirow{8}{*}{ResNet-56} & GAL-0.6 [37] & 93.26 \(\rightarrow\) 92.98 & 0.28 & 78.30M (37.6\%) & 0.75M (11.8\%) \\  & L1 [21] & 93.04 \(\rightarrow\) 93.06 & \(-\)0.02 & 90.90M (27.6\%) & 0.73M (14.1\%) \\  & HRank [31] & 93.26 \(\rightarrow\) 93.52 & \(-\)0.26 & 88.72M (29.3\%) & 0.71M (16.8\%) \\  & **FPAC** & 93.26 \(\rightarrow\) 94.35 & \(-\)1.09 & 90.35M (28.0\%) & 0.66M (22.3\%) \\  & NISP [24] & 93.26 \(\rightarrow\) 93.01 & 0.15 & 81.00M (35.5\%) & 0.49M (42.4\%) \\  & **FPAC** & 93.26 \(\rightarrow\) 93.71 & \(-\)0.45 & 65.94M (47.4\%) & 0.48M (42.8\%) \\  & CP [28] & 92.80 \(\rightarrow\) 91.80 & 1.00 & 62.00M (50.6\%) & - \\  & FPGM [23] & 93.59 \(\rightarrow\) 93.49 & 0.10 & 59.40M (52.6\%) & - \\  & GAL-0.8 [37] & 93.26 \(\rightarrow\) 90.36 & 2.90 & 49.99M (60.2\%) & 0.29M (65.9\%) \\  & HRank [31] & 93.26 \(\rightarrow\) 90.72 & 2.54 & 32.52M (74.1\%) & 0.27M (68.1\%) \\  & **FPAC** & 93.26 \(\rightarrow\) 92.37 & 0.89 & 34.78M (74.1\%) & 0.24M (70.0\%) \\ \hline \multirow{8}{*}{ResNet-110} & L1 [21] & 93.53 \(\rightarrow\) 93.30 & 0.23 & 155.00M (38.6\%) & 1.16M (32.4\%) \\  & HRank [31] & 93.50 \(\rightarrow\) 94.23 & \(-\)0.73 & 148.70M (41.2\%) & 1.04M (39.4\%) \\  & **FPAC** & 93.50 \(\rightarrow\) 94.52 & \(-\)1.02 & 140.54M (44.4\%) & 1.04M (39.4\%) \\  & FPGM [23] & 93.68 \(\rightarrow\) 93.85 & \(-\)0.27 & 121.00M (52.3\%) & - \\  & NISP [24] & 93.50 \(\rightarrow\) 93.32 & 0.18 & 142.17M (43.8\%) & 0.80M (43.3\%) \\  & GAL-0.5 [37] & 93.50 \(\rightarrow\) 92.55 & 0.95 & 130.20M (48.5\%) & 0.95M (44.8\%) \\  & HRank [31] & 93.50 \(\rightarrow\) 93.36 & 0.14 & 105.70M (58.2\%) & 0.70M (59.2\%) \\  & **FPAC** & 93.50 \(\rightarrow\) 93.49 & 0.01 & 71.69M (71.6\%) & 0.54M (68.3\%) \\ \hline \multirow{8}{*}{DenseNet-40} & GAL-0.01 [37] & 94.81 \(\rightarrow\) 94.29 & 0.52 & 182.92M (35.3\%) & 0.67M (35.6\%) \\  & Slimming [33] & 94.81 \(\rightarrow\) 94.81 & 0.00 & 190.00M (32.8\%) & 0.66M (36.5\%) \\  & HRank [31] & 94.81 \(\rightarrow\) 94.24 & 0.57 & 167.41M (40.8\%) & 0.66M (36.5\%) \\  & **FPAC** & 94.81 \(\rightarrow\) 94.51 & 0.30 & 173.39M (38.5\%) & 0.62M (40.1\%) \\  & HRank [31] & 94.81 \(\rightarrow\) 93.68 & 1.13 & 110.15M (61.0\%) & 0.48M (53.8\%) \\  & GAL-0.05 [37] & 94.81 \(\rightarrow\) 93.53 & 1.28 & 128.11M (54.7\%) & 0.45M (56.7\%) \\  & VP [32] & 94.11 \(\rightarrow\) 93.16 & 0.95 & 156.00M (44.8\%) & 0.42M (59.7\%) \\  & **FPAC** & 94.81 \(\rightarrow\) 93.66 & 1.15 & 113.08M (59.9\%) & 0.39M (61.9\%) \\ \hline \multirow{8}{*}{GoogLeNet} & L1 [21] & 95.05 \(\rightarrow\) 94.54 & 0.51 & 1.02B (32.9\%) & 3.51M (42.9\%) \\  & GAL-0.05 [37] & 95.05 \(\rightarrow\) 93.93 & 1.12 & 0.94B (38.2\%) & 3.12M (49.3\%) \\ \cline{1-1}  & **FPAC** & 95.05 \(\rightarrow\) 95.04 & 0.02 & 0.65B (57.2\%) & 2.85M (53.5\%) \\ \cline{1-1}  & GAL-ApoZ [37] & 95.05 \(\rightarrow\) 92.11 & 2.94 & 0.76B (50.0\%) & 2.85M (53.7\%) \\ \cline{1-1}  & HRank [31] & 95.05 \(\rightarrow\) 94.53 & 0.52 & 0.69B (54.9\%) & 2.74M (55.4\%) \\ \cline{1-1}  & HRank [31] & 95.05 \(\rightarrow\) 94.07 & 0.98 & 0.45B (70.4\%) & 1.86M (69.8\%) \\ \cline{1-1}  & **FPAC** & 95.05 \(\rightarrow\) 94.42 & 0.63 & 0.40B (73.9\%) & 2.09M (65.8\%) \\ \hline \end{tabular}
\end{table}