Developer Guide for Intel® Data Analytics Acceleration Library 2018

Local Contrast Normalization Forward Layer

Given a p-dimensional tensor X R n 1 x n 2 x ... x n p , two-dimensional tensor KR m 1 x m 2 , dimensions k 1 of size m 1 and k 2 of size m 2, and dimension f different from k 1 and k 2, the layer computes the p-dimensional tensor YR n 1 x n 2 x ... x n p such that:



See [Jarrett2009] for an exact definition of local contrast normalization.

The library supports four-dimensional input tensors XR n 1 x n 2 x n 3 x n 4 .

Problem Statement

Without loss of generality let's assume that forward local contrast normalization is applied to the last two dimensions.

The problem is to compute the tensor Y depending on whether the dimension f is set: