Explainable AI for Practitioners by Michael Munn & David Pitman

Author:Michael Munn & David Pitman [Michael Munn]
Language: eng
Format: epub
Publisher: O'Reilly Media, Inc.
Published: 2022-12-25T00:00:00+00:00

Figure 2-4. As the value of alpha varies from 0 to 1, we obtain a series of images creating a straight line path in image space from the baseline to the input image.

As increases and more information is introduced to our baseline image, the signal sent to the model and our confidence in what is actually contained in the image increases. When , at the baseline, there is, of course, no way for the model (or, anyone really) to be able to make an accurate prediction. There is no information in the image! However, as we increase and move along the straight line path the content of the image becomes more clear and the model can make a reasonable prediction.

If we think of this mathematically, the confidence of the model’s prediction is quantified in the value of the final softmax output layer. By calling prediction with our trained model on the interpolated images, we can directly examine the model’s confidence in the label sulfur-crested cockatoo’:

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