CAManim: Animating end-to-end network activation maps.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Machine Learning

Background:

• Deep neural networks, particularly Convolutional Neural Networks (CNNs), excel in image analysis but often function as black boxes.
• Understanding the internal workings and learned representations of complex CNNs is challenging for developers and end-users.
• Explainable Artificial Intelligence (XAI) methods, like Class Activation Maps (CAMs), aim to demystify CNN predictions and performance.

Purpose of the Study:

• To introduce CAManim, a novel XAI visualization technique for enhancing end-user comprehension of CNN predictions.
• To provide an end-to-end visualization of how CNNs progressively arrive at final layer activations.
• To propose a novel quantitative assessment metric, ybROAD, complementing qualitative explanations.

Main Methods:

• Developed CAManim, a method that animates CAM-based network activation maps across all CNN layers.
• Demonstrated CAManim's compatibility with various CAM-based methods and CNN architectures.
• Introduced the ybROAD metric, an expansion of the Remove and Debias (ROAD) metric for quantitative model assessment.

Main Results:

• CAManim effectively visualizes the end-to-end process of CNN predictions by animating activation maps.
• The method is versatile, working across different CAM techniques and CNN models.
• The combined qualitative (CAManim) and quantitative (ybROAD) assessments offer a more robust model evaluation.

Conclusions:

• CAManim significantly improves the interpretability and transparency of CNN models for end-users.
• The proposed ybROAD metric provides a novel quantitative approach to model assessment.
• These advancements foster greater trust and understanding in AI-driven predictions.