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[D] Help on understanding MobileNetV2 Research Paper

I am having trouble understanding a section from the MobileNetV2 paper.

In particular, section 3.2 Linear Bottlenecks, authors talk about how “it is easy to see that in general if a result of a layer transformation ReLU(Bx) has a non-zero volume S, the points mapped to interior S are obtained via a linear transformation B of the input, thus indicating that the part of the input space corresponding to the full dimensional output is limited to a linear transformation.” Is there a simpler way of explaining this?

Can I check my understanding, that this volume S is the volume created by the output tensor of ReLU(Bx), which each “pixel value” is a multi-dimensional vector and a point in the subspace, and all of these points form a volume? And if so, it is not clear to me why the interior points have any relevance to the argument.

submitted by /u/DinoHustler
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.