IAM

01stSEPTEMBER2019

READING

Marc Khoury, Dylan Hadfield-Menell. On the Geometry of Adversarial Examples. CoRR abs/1811.00525 (2018).

Khoury and Hadfield-Menell provide two important theoretical insights regarding adversarial robustness: it is impossible to be robust in terms of all norms, and adversarial training is sample inefficient. Specifically, they study robustness in relation to the problem’s codimension, i.e., the difference between the dimensionality of the embedding space (e.g., image space) and the dimensionality of the manifold (where the data is assumed to actually live on). Then, adversarial training is shown to be sample inefficient in high codimensions.

Also find this summary on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: