Respect the model: Fine-grained and Robust Explanation with Sharing
  Ratio Decomposition
Sangyu Han Yearim Kim Nojun Kwak
The truthfulness of existing explanation methods in authentically elucidating
the underlying model's decision-making process has been questioned. Existing
methods have deviated from faithfully representing the model, thus susceptible
to adversarial attacks. To address this, we propose a novel eXplainable AI
(XAI) method called SRD (Sharing Ratio Decomposition), which sincerely reflects
the model's inference process, resulting in significantly enhanced robustness
in our explanations. Different from the conventional emphasis on the neuronal
level, we adopt a vector perspective to consider the intricate nonlinear
interactions between filters. We also introduce an interesting observation
termed Activation-Pattern-Only Prediction (APOP), letting us emphasize the
importance of inactive neurons and redefine relevance encapsulating all
relevant information including both active and inactive neurons. Our method,
SRD, allows for the recursive decomposition of a Pointwise Feature Vector
(PFV), providing a high-resolution Effective Receptive Field (ERF) at any
layer.