ClusterXplain: a Clustering-based Tool for DNN components Debugging

We introduce ClusterXplain, a tool offering 36 distinct pipelines for debugging deep neural network (DNN) failures. Building on our previous approach, SAFE, ClusterXplain leverages transfer learning models to extract features from failure-inducing images. These features are used to cluster corresponding images using advanced algorithms, including density-based methods, to identify the distinct scenarios in which the DNN may fail. While SAFE focused on a single pipeline configuration, ClusterXplain explores diverse combinations of feature extraction methods, clustering algorithms, and dimensionality reduction techniques to improve DNN debugging. The results show that ClusterXplain achieves high accuracy in scenario identification, offering actionable insights to engineers to diagnose and mitigate issues in safety-critical applications. A demo video of ClusterXplain is available at https://youtu.be/DU94c-lIwys.