Why not scale values when attribution values smaller than 1e-5?

When displaying the attribution, you normalise and scale the values.

However, do you skip normalising if the scaling factor (which is the max value after the outliers) is below 1e-5?

def _normalize_scale(attr: ndarray, scale_factor: float):
    if abs(scale_factor) < 1e-5:
        warnings.warn(
            "Attempting to normalize by value approximately 0, skipping normalization."
            "This likely means that attribution values are all close to 0."
        )
     ....

[andreimargeloiu]

Use-case My use-case is interpreting robust model (they are trained using adversarial training [1]). Such models are trained on adversarial inputs.

On robust models, the gradients with respect to the input are very small (see picture below), where the s axis represents the attributions before rescaling. Notice that the range is around 1e-3. Using SmoothGrad, the gradients are around 1e-5, 1e-6 -> which creates issues with Captum.

Issue with current warning For people investigation interpretability on robust models, it’s essential to be able to plot them, despite potential errors associated with floating-point arithmetic.

In Jupyter this warning wasn’t printed, which took me hours to dig into Captum and understand why the saliency map was essentially white (because the inputs weren’t scaled)

Potential solution: It would be good to allow power-user to bypass this warning (either through a parameter), or simply disable the check.

[1] https://arxiv.org/pdf/1706.06083.pdf

[andreimargeloiu]

Thank you for the heads up! @vivekmig, please go ahead as you initially proposed and plan this change for a future release 😃