Introducing StylEx: A New Approach for Visual Explanation of Classifiers

0

Introducing StylEx: A New Approach for Visual Explanation of Classifiers

Introducing StylEx: A New Approach for Visual Explanation of Classifiers

“Neural networks can perform certain tasks remarkably well, but understanding how they reach their decisions — e.g., identifying which signals in an image cause a model to determine it to be of one class and not another — is often a mystery. Explaining a neural model’s decision process may have high social impact in certain areas, such as analysis of medical images and autonomous driving, where human oversight is critical. These insights can also be helpful in guiding health care providers, revealing model biases, providing support for downstream decision makers, and even aiding scientific discovery.

Previous approaches for visual explanations of classifiers, such as attention maps (e.g., Grad-CAM), highlight which regions in an image affect the classification, but they do not explain what attributes within those regions determine the classification outcome: For example, is it their color? Their shape? Another family of methods provides an explanation by smoothly transforming the image between one class and another (e.g., GANalyze). However, these methods tend to change all attributes at once, thus making it difficult to isolate the individual affecting attributes…”

Source: ai.googleblog.com/2022/01/introducing-stylex-new-approach-for.html

February 3, 2022
0 Comments
Inline Feedbacks
View all comments

Subscribe to our Digest