Scale Preserving Automatic Concept Extraction

Scale-preserving automatic concept extraction (SPACE) aims to improve the interpretability of complex machine learning models, particularly Convolutional Neural Networks (CNNs), by generating human-understandable explanations of their decision-making processes. Current research focuses on developing algorithms that avoid distortions in scale when extracting concepts from data, such as images in industrial quality control, ensuring accurate representation of crucial features. This work is significant for enhancing the reliability and trustworthiness of AI systems in critical applications, providing valuable insights into model behavior and facilitating more effective debugging and improvement.