Immersive Neural Network Exploration: A VR Approach to Human-Centered AI Understanding

In today’s rapidly evolving artificial intelligence (AI) landscape, the complexity of neural networks, especially in deep learning, presents significant challenges for intuitive human understanding. Compelling visualization methods have become crucial with AI systems integrating into everyday experiences and environments. Virtual Reality (VR), as an immersive and interactive technology, offers a novel approach to visualizing intricate AI processes. This work introduces transformative updates to the DeepVisionVR platform, a pioneering tool for 3D visualization of Convolutional Neural Networks (CNNs) in VR. A cornerstone of our enhancements is the Sensitivity Analysis module, which offers real-time interactivity, allowing users to adjust pixels within the VR space, shedding light on the intricacies of model outcomes. In addition, advanced model interpretation methodologies have been integrated, including Integrated Gradients, GradientShap, and Occlusion, enriching the depth of insight into model rationale. Our Adversarial Analysis, utilizing the Fast Gradient Sign Method (FGSM), unveils potential weak points in models, emphasizing their vulnerability to minor input alterations. The new features aim to provide a deeper understanding of how neural networks interpret and react to various inputs, thereby bridging the gap between complex machine learning models and human interpretability.