Understand
Rescaling a vector $\vec{\delta} \in \mathbb{R}^n$ to a desired length is a common operation in many areas such as data science and machine learning.
- When the rescaled perturbation $\eta \vec{\delta}$ is added to a starting point $\vec{x} \in D$ (where $D$ is the data domain, e.g.
- $D = [0, 1]^n$), the resulting vector $\vec{v} = \vec{x} + \eta \vec{\delta}$ will in general not be in $D$.
- To enforce that the perturbed vector $v$ is in $D$, the values of $\vec{v}$ can be clipped to $D$.
Built on
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise
Evgenia Rusak, Lukas Schott, Roland Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2001
Earlier work this paper cites.
NumPy: A guide to NumPy
Travis Oliphant · 2006
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