In the [GradCAM paper](https://arxiv.org/pdf/1610.02391.pdf) section 3 they implicitly propose that two things are needed to understand which areas of an input image contribute most to the output class (in a multi-label classification problem). That is:

- $A^k$ the final feature maps
- $\alpha_k^c$ the average pooled partial derivatives of the output class scores $y^c$ with respect to the the final feature maps $A_k$.

The second point is clear to me. The stronger the derivative, the more important the $k$th channel of the final feature maps is.

The first point is not, because the implicit assumption is that non-zero activations have more significance than activations close to zero. I know it's tempting to take that as a given, but for me it's not so obvious. After all, neurons have biases, and a bias can arbitrarily shift the reference point, and hence what 0 means. We can easily transform two neurons [0, 1] to [1, 0] with a linear transformation.

So why should it matter which regions of the final feature maps are strongly activated?