Model Immunization from a Condition Number Perspective

Amber Yijia Zheng, Cedar Site Bai, Brian Bullins, Raymond Yeh

Model Immunization from a Condition Number Perspective: 9 upvotes on Hugging Face Daily Papers, #22 of 48 papers on 2025-06-10. Day-by-day upvote history.

Model immunization aims to pre-train models that are difficult to fine-tune on harmful tasks while retaining their utility on other non-harmful tasks. Though prior work has shown empirical evidence for immunizing text-to-image models, the key understanding of when immunization is possible and a precise definition of an immunized model remain unclear. In this work, we propose a framework, based on the condition number of a Hessian matrix, to analyze model immunization for linear models. Building on this framework, we design an algorithm with regularization terms to control the resulting condition numbers after pre-training. Empirical results on linear models and non-linear deep-nets demonstrate the effectiveness of the proposed algorithm on model immunization. The code is available at https://github.com/amberyzheng/model-immunization-cond-num.

Paper page on Hugging Face · arXiv

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