# Filter Pruning via Attention Consistency on Feature Maps
## 5 Ablation Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Baseline** & **Method** & **Top-1(\%)** & **Top-1(\%)** & **Top-5(\%)** & **Top-5(\%)** & **FLOPs(PR)** & **Params(PR)** \\ \hline \multirow{6}{*}{ResNet-18} & Sampling [27] & \(69.76\to 67.38\) & 2.38 & \(89.55\to 88.43\) & 1.12 & 1.28B (29.3\%) & 6.57M (43.8\%) \\  & SFP [22] & \(70.28\to 67.10\) & 3.18 & \(89.63\to 87.78\) & 1.85 & 1.05B (41.8\%) & - \\  & FPGM [23] & \(70.28\to 68.41\) & 1.87 & \(89.63\to 88.48\) & 1.15 & 1.05B (41.8\%) & - \\  & Hrank [31] & \(70.28\to 68.56\) & 1.72 & \(89.63\to 88.58\) & 1.05 & 1.01B (44.2\%) & 6.23M (46.7\%) \\  & DCP [34] & \(69.76\to 67.35\) & 2.41 & \(89.55\to 88.45\) & 1.10 & 0.98B (45.8\%) & 6.19M (46.6\%) \\  & **FPAC** & \(70.28\to 68.91\) & 1.67 & \(89.64\to 88.64\) & 1.00 & 0.93B (48.6\%) & 6.00M (48.7\%) \\ \hline \multirow{6}{*}{ResNet-18} & SSS-32 [35] & \(76.12\to 74.18\) & 1.94 & \(92.87\to 91.91\) & 0.96 & 2.82B (31.1\%) & 18.60M (27.3\%) \\  & CP [28] & \(74.99\to 72.84\) & 2.15 & \(92.20\to 90.80\) & 1.40 & 2.73B (32.8\%) & - \\  & SFP [22] & \(76.15\to 74.61\) & 1.54 & \(92.87\to 92.06\) & 0.81 & 2.38B (41.2\%) & - \\  & FFGM [23] & \(76.15\to 75.59\) & 0.56 & \(92.87\to 92.63\) & 0.24 & 2.36B (42.2\%) & - \\  & SSS-26 [23] & \(76.12\to 71.82\) & 4.30 & \(92.87\to 90.79\) & 2.08 & 2.33B (43.3\%) & 15.60M (38.9\%) \\  & GAL-0.5 [37] & \(76.15\to 71.95\) & 4.20 & \(92.87\to 90.04\) & 2.83 & 2.33B (43.3\%) & 21.20M (17.0\%) \\  & HRank [31] & \(76.15\to 74.98\) & 1.17 & \(92.87\to 92.33\) & 0.54 & 2.30B (44.0\%) & 16.15M (36.8\%) \\  & NISP [24] & - & 0.89 & - & - & - & 2.29B (44.0\%) & 14.38M (43.8\%) \\  & **FPAC** & \(76.15\to 75.62\) & 0.53 & \(92.87\to 92.63\) & 0.24 & 2.26B (45.0\%) & 15.09M (40.9\%) \\  & FPGM [23] & \(76.15\to 74.83\) & 1.32 & \(92.87\to 92.32\) & 0.55 & 1.90B (53.5\%) & - \\  & GDP-0.6 [36] & \(75.13\to 71.19\) & 3.94 & \(92.87\to 90.71\) & 2.16 & 1.88B (54.3\%) & - \\  & GAL-0.5-joint [37] & \(76.15\to 71.80\) & 4.35 & \(92.87\to 90.82\) & 2.05 & 1.84B (55.2\%) & 19.31M (24.4\%) \\  & ThNet [26] & \(72.88\to 71.01\) & 1.87 & \(91.94\to 90.30\) & 1.64 & 1.82B (55.8\%) & 12.40M (51.6\%) \\  & GAL-1 [37] & \(76.15\to 69.88\) & 6.27 & \(92.87\to 90.14\) & 2.73 & 1.58B (61.6\%) & 14.67M (42.4\%) \\  & GDP-0.5 [36] & \(75.13\to 69.58\) & 6.54 & \(92.87\to 90.14\) & 2.73 & 1.57B (61.8\%) & - \\  & HRank [31] & \(76.15\to 71.98\) & 4.17 & \(92.87\to 91.01\) & 1.86 & 1.55B (62.3\%) & 13.77M (46.1\%) \\  & **FPAC** & \(76.15\to 74.17\) & 1.85 & \(92.87\to 91.84\) & 1.03 & 1.52B (63.0\%) & 11.05M (56.7\%) \\  & GAL-1-joint [37] & \(76.15\to 69.31\) & 6.85 & \(92.87\to 89.12\) & 3.75 & 1.11B (73.0\%) & 10.21M (60.0\%) \\  & ThiNet [26] & \(72.88\to 68.42\) & 4.46 & \(91.94\to 88.30\) & 3.64 & 1.10B (73.2\%) & 8.66M (66.1\%) \\  & HRank [31] & \(76.15\to 69.10\) & 7.05 & \(92.87\to 89.58\) & 3.29 & 0.98B (76.2\%) & 8.27M (67.6\%) \\  & **FPAC** & \(76.15\to 72.30\) & 3.85 & \(92.87\to 90.74\) & 2.13 & 0.95B (76.7\%) & 8.02M (68.6\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pruning results of ResNet on ImageNet. “Top-1”: accuracy. “Top-1\(\downarrow\)”: decrease in accuracy, smaller is better. A negative value here indicates an improved model accuracy. “Top-5\(\downarrow\)”: decrease of Top-5. “FLOPs(PR)”: FLOPs(pruning ratio). “Params(PR).”: parameters(pruning ratio). “-”: results not reported in the corresponding paper.