Explain an image classifier result using neural-symbolic learning with Greybox XAI

Explain an image classifier result using neural-symbolic learning with Greybox XAI

Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
arXiv paper abstract https://arxiv.org/abs/2209.14974v1
arXiv PDF paper https://arxiv.org/pdf/2209.14974v1.pdf
GitHub https://github.com/AdriBento/Greybox-

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities, their functioning does not allow a detailed explanation of their behavior.

… present the Greybox XAI, a framework that composes a DNN and a transparent model thanks to the use of a symbolic Knowledge Base (KB).

… extract a KB from the dataset and use it to train a transparent model (i.e., a logistic regression).

An encoder-decoder architecture is trained on RGB images to produce an output similar to the KB used by the transparent model.

Once the two models are trained independently, they are used compositionally to form an explainable predictive model.

… show how this new architecture is accurate and explainable in several datasets.

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I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. Am recognized innovator with 66 patents.