CNN Explainability with Multivector Tucker Saliency Maps for Self-Supervised Models

Interpreting the decisions of Convolutional Neural Networks (CNNs) is essential for understanding their behavior, yet explainability remains a significant challenge, particularly for self-supervised models. Most existing methods for generating saliency maps rely on ground truth labels, restricting their use to supervised tasks. EigenCAM is the only notable label-independent alternative, leveraging Singular Value Decomposition to generate saliency maps applicable across CNN models, but it does not fully exploit the tensorial structure of feature maps. In this work, we introduce the Tucker Saliency Map (TSM) method, which applies Tucker tensor decomposition to better capture the inherent structure of feature maps, producing more accurate singular vectors and values. These are used to generate high-fidelity saliency maps, effectively highlighting objects of interest in the input. We further extend EigenCAM and TSM into multivector variants -Multivec-EigenCAM and Multivector Tucker Saliency Maps (MTSM)- which utilize all singular vectors and values, further improving saliency map quality. Quantitative evaluations on supervised classification models demonstrate that TSM, Multivec-EigenCAM, and MTSM achieve competitive performance with label-dependent methods. Moreover, TSM enhances explainability by approximately 50% over EigenCAM for both supervised and self-supervised models. Multivec-EigenCAM and MTSM further advance state-of-the-art explainability performance on self-supervised models, with MTSM achieving the best results.

Human Biophysics as Network Weights: Conditional Generative Models for Ultra-fast Simulation

Simulations of biophysical systems have provided a huge contribution to our fundamental understanding of human physiology and remain a central pillar for developments in medical devices and human machine interfaces. However, despite their successes, such simulations usually rely on highly computationally expensive numerical modelling, which is often inefficient to adapt to new simulation parameters. This limits their use in dynamic models of human behavior, for example in modelling the electric fields generated by muscles in a moving arm. We propose the alternative approach to use conditional generative models, which can learn complex relationships between the underlying generative conditions whilst remaining inexpensive to sample from. As a demonstration of this concept, we present BioMime, a hybrid architecture that combines elements of deep latent variable models and conditional adversarial training to construct a generative model that can both transform existing data samples to reflect new modelling assumptions and sample new data from a conditioned distribution. We demonstrate that BioMime can learn to accurately mimic a complex numerical model of human muscle biophysics and then use this knowledge to continuously sample from a dynamically changing system in real-time. We argue that transfer learning approaches with conditional generative models are a viable solution for dynamic simulation with any numerical model.