Knowledge-Guided Masked Autoencoder with Linear Spectral Mixing and Spectral-Angle-Aware Reconstruction

TL;DR

This paper introduces a knowledge-guided masked autoencoder that embeds physical domain knowledge into the self-supervised learning process, improving interpretability, reconstruction quality, and stability, especially with limited supervision.

Contribution

It presents a novel ViT-based autoencoder that integrates physical constraints like LSMM and SAM into the training, enhancing interpretability and physical consistency of learned representations.

Findings

Significantly improved reconstruction quality.

Enhanced downstream task performance.

More stable training with limited supervision.

Abstract

Integrating domain knowledge into deep learning has emerged as a promising direction for improving model interpretability, generalization, and data efficiency. In this work, we present a novel knowledge-guided ViT-based Masked Autoencoder that embeds scientific domain knowledge within the self-supervised reconstruction process. Instead of relying solely on data-driven optimization, our proposed approach incorporates the Linear Spectral Mixing Model (LSMM) as a physical constraint and physically-based Spectral Angle Mapper (SAM), ensuring that learned representations adhere to known structural relationships between observed signals and their latent components. The framework jointly optimizes LSMM and SAM loss with a conventional Huber loss objective, promoting both numerical accuracy and geometric consistency in the feature space. This knowledge-guided design enhances reconstruction fidelity, stabilizes training under limited supervision, and yields interpretable latent representations grounded in physical principles. The experimental findings indicate that the proposed model substantially enhances reconstruction quality and improves downstream task performance, highlighting the promise of embedding physics-informed inductive biases within transformer-based self-supervised learning.