Muhammad Rashid

Muhammad Rashid

Machine Learning Researcher

ShapBPT in Perspective: A Consolidated Review and an eXplainable Anomaly Detection Case Study

Published in QualITA Workshop | ICPE 2026 (ACM), 2026

Paper Code

This paper presents ShapBPT in Perspective, a consolidated review and practical case study of ShapBPT for eXplainable Anomaly Detection (XAD). The work bridges hierarchical Shapley-based explanations with real-world anomaly detection systems, demonstrating how structured feature attributions can improve the interpretability of black-box models.

Unlike traditional explanation methods based on fixed partitions, ShapBPT leverages a data-aware Binary Partition Tree (BPT) to generate multiscale, semantically aligned explanations. This enables more faithful localization of anomalous regions and provides clearer insight into model behavior.

🔗 Resources

📌 Key Contributions

  1. A consolidated review of ShapBPT for explainable computer vision.
  2. A real-world case study on explainable anomaly detection (XAD).
  3. A unified framework connecting hierarchical Shapley explanations with anomaly detection systems.
  4. Release of open-source implementation (XAD) for reproducibility.

⚙️ Method Overview

ShapBPT explains anomaly detection decisions by assigning attribution scores to image regions using a hierarchical, data-driven partitioning strategy.

Example Explanation

XAD Workflow

This approach enables explanations to follow intrinsic image structure, improving robustness compared to grid-based or pixel-wise attribution methods.

🧪 Experimental Setup

  • Task: Explainable Anomaly Detection
  • Method: ShapBPT
  • Model Type: Black-box anomaly detection models (e.g., VAE-GAN)
  • Explanation Level: Pixel-level and region-level attribution

👥 Authors

Sr. No.Author NameAffiliationGoogle Scholar
1.Muhammad RashidUniversity of Torino, Dept. of Computer Science, Torino, ItalyMuhammad Rashid
2.Elvio G. AmparoreUniversity of Torino, Dept. of Computer Science, Torino, ItalyElvio G. Amparore
3.Enrico FerrariRulex Innovation Labs, Rulex Inc., Genova, ItalyEnrico Ferrari
4.Damiano VerdaRulex Innovation Labs, Rulex Inc., Genova, ItalyDamiano Verda

🖼️ Sample Output

🔑 Keywords

ShapBPT · Explainable Anomaly Detection · XAI · Shapley Values · Binary Partition Trees · Computer Vision · ICPE 2026

📖 Citation (BibTeX)

@inproceedings{rashid2026shapbptperspective,
  title     = {ShapBPT in Perspective: A Consolidated Review and an eXplainable Anomaly Detection Case Study},
  author    = {Rashid, Muhammad and Amparore, Elvio G.},
  booktitle = {Proceedings of the QualITA Workshop at ICPE 2026},
  year      = {2026},
  publisher = {ACM},
  doi       = {10.1145/3777911.3800638},
  url       = {https://doi.org/10.1145/3777911.3800638}
}