The article presents a new method called Decomposition-based Explainable AI (DXAI) for explaining neural network image classification. The key idea is to decompose the input image into two additive parts: a class-agnostic part that does not contain class-specific information, and a class-distinct part that holds the discriminative features responsible for the classification.
The authors argue that standard heatmap-based XAI methods are less informative in scenarios where the classification relies on dense, global, and additive features, such as color or texture. In contrast, the DXAI decomposition can better explain such cases.
The authors formulate the DXAI problem as an optimization to find the closest class-agnostic image to the input. They propose an approximate solution using style transfer GANs, where the class-distinct part is isolated in the first generator branch, while the subsequent branches generate the class-agnostic components.
The training process encourages the generators to isolate the class-distinct features, using an α-blending mechanism and various loss functions. The authors show qualitative and quantitative results on several datasets, demonstrating the advantages of DXAI over heatmap-based explanations, especially for classification tasks relying on additive and global features.
The authors also discuss limitations of their approach, such as the lack of a natural pixel-wise importance ranking, and suggest potential improvements using diffusion-based generative models.
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by Elnatan Kada... at arxiv.org 04-01-2024
https://arxiv.org/pdf/2401.00320.pdfDeeper Inquiries