To what extent does scaling the backbone size (e.g., ViT-B vs. ViT-L) in multimodal models improve robustness

Abstract

Abstract: Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to adversarial attacks due to limitations in training data, which typically include only normal, unlabeled samples. This study introduces PatchGuard, an adversarially robust AD and AL method that incorporates pseudo anomalies with localization masks within a Vision Transformer (ViT)-based architecture to address these vulnerabilities. We begin by examining the essential properties

Research Question

To what extent does scaling the backbone size (e.g., ViT-B vs. ViT-L) in multimodal models improve robustness to distribution shifts in brain MRI anomaly localization, as measured by Dice score degradation across different imaging protocols?

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