On Transfer of Adversarial Robustness from Pretraining to Downstream
Tasks
- URL: http://arxiv.org/abs/2208.03835v2
- Date: Mon, 9 Oct 2023 16:48:57 GMT
- Title: On Transfer of Adversarial Robustness from Pretraining to Downstream
Tasks
- Authors: Laura Fee Nern, Harsh Raj, Maurice Georgi, Yash Sharma
- Abstract summary: We show that the robustness of a linear predictor on downstream tasks can be constrained by the robustness of its underlying representation.
Our results offer an initial step towards characterizing the requirements of the representation function for reliable post-adaptation performance.
- Score: 1.8900691517352295
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: As large-scale training regimes have gained popularity, the use of pretrained
models for downstream tasks has become common practice in machine learning.
While pretraining has been shown to enhance the performance of models in
practice, the transfer of robustness properties from pretraining to downstream
tasks remains poorly understood. In this study, we demonstrate that the
robustness of a linear predictor on downstream tasks can be constrained by the
robustness of its underlying representation, regardless of the protocol used
for pretraining. We prove (i) a bound on the loss that holds independent of any
downstream task, as well as (ii) a criterion for robust classification in
particular. We validate our theoretical results in practical applications, show
how our results can be used for calibrating expectations of downstream
robustness, and when our results are useful for optimal transfer learning.
Taken together, our results offer an initial step towards characterizing the
requirements of the representation function for reliable post-adaptation
performance.
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