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Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer
As such, we propose the direct perturbation of physical parameters that underly image formation: lighting and geometry.
An empirical analysis of the optimization of deep network loss surfaces
The success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions.
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