Comparative Impact of Implicit Noise Modeling and Explicit Soft Label Smoothing on Adversarial Robustness in Tabular Foundation

We introduce a Noise-based prior Learning (NoL) approach for training neural networks that are intrinsically robust to adversarial attacks. We find that the implicit generative modeling of random noise with the same loss function used during posterior maximization, improves a model's understanding of the data manifold furthering adversarial robustness. We evaluate our approach's efficacy and provide a simplistic visualization tool for understanding adversarial data, using Principal Component Analysis. Our analysis reveals that adversarial robustness, in general, manifests in models with higher

Research goal: What is the comparative impact of implicit noise modeling versus explicit soft label smoothing on the adversarial robustness of tabular foundation models evaluated via accuracy degradation on corrupted datasets?

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