Model Immunization from a Condition Number Perspective
Authors: name: Amber Yijia Zheng, name: Cedar Site Bai, name: Brian Bullins, name: Raymond A. Yeh

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.