<p>In <a href="https://github.com/mlpack/mlpack/pull/703#discussion_r68450940">src/mlpack/methods/edge_boxes/feature_extraction_impl.hpp</a>:</p>
<pre style='color:#555'>> + dx.slice(i).col(0) = InImage.slice(i).col(1) - InImage.slice(i).col(0);
> + dx.slice(i).col(InImage.n_cols - 1) = InImage.slice(i).col(InImage.n_cols - 1)
> + - InImage.slice(i).col(InImage.n_cols - 2);
> +
> + for (size_t j = 1; j < InImage.n_cols-1; j++)
> + dx.slice(i).col(j) = 0.5 * ( InImage.slice(i).col(j+1) - InImage.slice(i).col(j) );
> +
> + // do same for dy.
> + dy.slice(i).row(0) = InImage.slice(i).row(1) - InImage.slice(i).row(0);
> + dy.slice(i).row(InImage.n_rows - 1) = InImage.slice(i).row(InImage.n_rows - 1)
> + - InImage.slice(i).row(InImage.n_rows - 2);
> +
> + for (size_t j = 1; j < InImage.n_rows-1; j++)
> + dy.slice(i).row(j) = 0.5 * ( InImage.slice(i).row(j+1) - InImage.slice(i).row(j) );
> + }
> +
</pre>
<p>I think by usual, we would apply convolution on the matrix directly, cnn of mlpack implement convolution algo, maybe you can use it to find out the gradient.</p>
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