Reprogrammers are robust learners

Ongoing Project


In this work, we investigate the effect of adversarial reprogramming on the robustness of the model upon transfer. We further are interested in adding a robustness objective into the constrained optimization process of reprogramming. We further demonstrate the effect of scaling on reprogramming and do an in-depth cost analysis breakdown comparison of reprogramming and fine-tuning.


[1] Elsayed, Gamaleldin F., Ian Goodfellow, and Jascha Sohl-Dickstein. “Adversarial reprogramming of neural networks.” arXiv preprint arXiv:1806.11146 (2018).

[2] Wortsman, Mitchell, Gabriel Ilharco, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, and Ludwig Schmidt. “Robust fine-tuning of zero-shot models.” arXiv preprint arXiv:2109.01903 (2021).