# Why should the baseline's prediction be near zero, according to the Integrated Gradients paper?

I am trying to understand Intagrated Gradients, but have difficulty in understanding the authors' claim (in section 3, page 3):

For most deep networks, it is possible to choose a baseline such that the prediction at the baseline is near zero ($$F(x') \approx 0$$). (For image models, the black image baseline indeed satisfies this property.)

They are talking about a function $$F : R^n \rightarrow [0, 1]$$ (in 2nd paragraph of section 3), and if you consider a deep learning classification model, the final layer would be a softmax layer. Then, I suspect for image models, the prediction at the baseline should be close to $$1/k$$, where $$k$$ is the number of categories. For CIFAR10 and MNIST, this would equal to $$1/10$$, which is not very close to $$0$$. I have a binary classification model on which I am interested in applying the Integrated Gradients algorithm. Can the baseline output of $$0.5$$ be a problem?

Another related question is, why did they choose a black image as the baseline in the first place? The parameters in image classification models (in a convolution layer) are typically initialized around $$0$$, and the input is also normalized. Therefore, image classification models do not really care about the sign of inputs. I mean we could multiply all the training and test inputs with $$-1$$, and the model would learn the task equivalently. I guess I can find other neutral images other than a black one. I suppose we could choose a white image as the baseline, or maybe the baseline should be all zero after normalization?

• Regarding your last paragraph, in remark 1 (in the paper), they talk about the rationale behind a baseline and what it represents.
– nbro
Jun 21, 2020 at 13:46
• @nbro I can see that a black image does imply "the absence of the cause", and it also might be "neutral". I still think a white image or random noise for example also could represent something neutral. In fact, I asked this question because the medical images I am working on have both positive and negative pixels, and the offset is kind of arbitrary. Maybe I will see mean pixel value and use it for the baseline. Jun 22, 2020 at 4:24