IEEE Computational Intelligence Magazine - May 2023 - 87

The first ablation experiment was
FIGURE 7. Comparison of the number of parameters between the group convolution and the
standard convolution.
to investigate the effectiveness of the
SENet module. Ten individuals in the
final population are randomly selected
for both experiments, and all of these
individuals contain group convolution
and SENet modules.
performed to verify the impact of the
group convolution on the number of
parameters. The number of parameters
of each individual is recorded first.
Then, the group convolution of each
individual is transformed into a standard
convolution while keeping the
remaining topology the same, and the
corresponding number of parameters is
recorded. The comparison results are
showninFig. 7. The green bar represents
the group convolution results,
and the blue bar represents the standard
convolution results. Fig. 7 shows
that the group convolution consumes
fewer parameters than the standard
convolution, and each individual with
the group convolution has approximately
half the number of parameters.
Therefore, the group convolution
effectively reduces the number of
parameters.
In the second ablation experiment,
FIGURE 8. Performance comparison between original network architectures and those without
SENet modules.
the effectiveness of the SENet module
in improving the test accuracy and
reducing the number of parameters is
verified. The test accuracy and number
of parameters of each individual are
recorded through 10 independent trials.
Then, all SENet modules of each
individual are removed, and the test
accuracy and number of parameters are
recorded. The comparison results
regarding the test accuracy and the
number of parameters are shown in
Fig. 8(a) and (b), respectively. In Fig. 8,
thebluelineand thebluebar represent
the original network architectures, and
the green line and the green bar represent
the architectures without SENet
modules. Fig. 8(a) shows that compared
to the original network architectures,
the accuracy of the networks without
the SENet modules is substantially
reduced, indicating that the SENet
module improves the accuracy. Fig. 8
(b) indicates that the addition ofSENet
module only slightly increases the
number of parameters. These results
demonstrate that the SENet module
remarkably improves the classification
performance of the network architecture
while adding only a moderate
number ofparameters.
MAY 2023 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 87

IEEE Computational Intelligence Magazine - May 2023

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