  Deep Neural Networks are powerful tools for understanding complex patterns
and making decisions. However, their black-box nature impedes a complete
understanding of their inner workings. Saliency-Guided Training (SGT) methods
try to highlight the prominent features in the model's training based on the
output to alleviate this problem. These methods use back-propagation and
modified gradients to guide the model toward the most relevant features while
keeping the impact on the prediction accuracy negligible. SGT makes the model's
final result more interpretable by masking input partially. In this way,
considering the model's output, we can infer how each segment of the input
affects the output. In the particular case of image as the input, masking is
applied to the input pixels. However, the masking strategy and number of pixels
which we mask, are considered as a hyperparameter. Appropriate setting of
masking strategy can directly affect the model's training. In this paper, we
focus on this issue and present our contribution. We propose a novel method to
determine the optimal number of masked images based on input, accuracy, and
model loss during the training. The strategy prevents information loss which
leads to better accuracy values. Also, by integrating the model's performance
in the strategy formula, we show that our model represents the salient features
more meaningful. Our experimental results demonstrate a substantial improvement
in both model accuracy and the prominence of saliency, thereby affirming the
effectiveness of our proposed solution.
