  In this study, we employ a convolutional neural network to classify
gravitational waves originating from core-collapse supernovae. Training is
conducted using spectrograms derived from three-dimensional numerical
simulations of waveforms, which are injected onto real noise data from the
third observing run of both Advanced LIGO and Advanced Virgo. To gain insights
into the decision-making process of the model, we apply class activation
mapping techniques to visualize the regions in the input image that are
significant for the model's prediction. The class activation maps reveal that
the model's predictions predominantly rely on specific features within the
input spectrograms, namely, the $g$-mode and low-frequency modes. The
visualization of convolutional neural network models provides interpretability
to enhance their reliability and offers guidance for improving detection
efficiency.
