Jun 28, 2020 � Two causes are identified and qualitatively distinguished: morphological similarity and non-essential information interference. The former cause�...
Jun 28, 2020 � PDF | The causal explanation of image misclassifications is an understudied niche, which can potentially provide valuable insights in model.
The causal explanation of image misclassifications is an understudied niche, which can potentially provide valuable insights in model interpretability and�...
Results indicate that humans do not “minimally edit” images when generating counterfactual explanations. Instead, they make larger, “meaningful” edits that�...
Input: a ranked list OR a saliency landscape. 2. From the highest ranked pixels, add pixels greedily. 3. Can be spatially-aware or agnostic.
Sep 22, 2022 � The neural network scores a 98.9% accuracy on the dataset. However, when I try to use an image of my own, it always classifies the input as 'A'.
Explainable AI (XAI) methods contribute to under- standing the behavior of deep neural networks (DNNs), and have attracted interest recently.
The changes made to the image to fix classification errors explain the causes of misclassification and allow adjusting the model and the data set to obtain�...
Missing: Causal | Show results with:Causal
Aug 1, 2023 � Moreover, we explore causal relationships in model explanations and discuss approaches dedicated to explaining cross-domain classifiers. The�...
