Reliable or Deceptive? Investigating Gated Features for Smooth Visual Explanations in CNNs
CoRR(2024)
Abstract
Deep learning models have achieved remarkable success across diverse domains.
However, the intricate nature of these models often impedes a clear
understanding of their decision-making processes. This is where Explainable AI
(XAI) becomes indispensable, offering intuitive explanations for model
decisions. In this work, we propose a simple yet highly effective approach,
ScoreCAM++, which introduces modifications to enhance the promising ScoreCAM
method for visual explainability. Our proposed approach involves altering the
normalization function within the activation layer utilized in ScoreCAM,
resulting in significantly improved results compared to previous efforts.
Additionally, we apply an activation function to the upsampled activation
layers to enhance interpretability. This improvement is achieved by selectively
gating lower-priority values within the activation layer. Through extensive
experiments and qualitative comparisons, we demonstrate that ScoreCAM++
consistently achieves notably superior performance and fairness in interpreting
the decision-making process compared to both ScoreCAM and previous methods.
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