Spectral Introspection Identifies Group Training Dynamics in Deep Neural Networks for Neuroimaging

TL;DR

This paper introduces a novel spectral introspection method for deep neural networks applied to neuroimaging, enabling real-time analysis of training dynamics and group-specific gradient behaviors.

Contribution

The work presents a new gradient decomposition technique using spectral analysis that allows on-the-fly understanding of training dynamics and group differences in neuroimaging models.

Findings

Gradient spectra differ between schizophrenia and control groups.

Spectral analysis reveals training dynamics related to group-specific features.

Method enables real-time, group-aware model introspection.

Abstract

Neural networks, whice have had a profound effect on how researchers study complex phenomena, do so through a complex, nonlinear mathematical structure which can be difficult for human researchers to interpret. This obstacle can be especially salient when researchers want to better understand the emergence of particular model behaviors such as bias, overfitting, overparametrization, and more. In Neuroimaging, the understanding of how such phenomena emerge is fundamental to preventing and informing users of the potential risks involved in practice. In this work, we present a novel introspection framework for Deep Learning on Neuroimaging data, which exploits the natural structure of gradient computations via the singular value decomposition of gradient components during reverse-mode auto-differentiation. Unlike post-hoc introspection techniques, which require fully-trained models for evaluation, our method allows for the study of training dynamics on the fly, and even more interestingly, allow for the decomposition of gradients based on which samples belong to particular groups of interest. We demonstrate how the gradient spectra for several common deep learning models differ between schizophrenia and control participants from the COBRE study, and illustrate how these trajectories may reveal specific training dynamics helpful for further analysis.