Advantages and limitations
In the previous section, we covered the practical aspects of TCAV. TCAV is indeed a very interesting and novel approach to explaining complex deep learning models. Although it has many advantages, unfortunately, I did find some limitations in terms of the current framework that can definitely be improved in the revised version.
Advantages
Let's discuss the following advantages first:
- As you have previously seen with the LIME framework in Chapter 4, LIME for Model Interpretability (which generates explanations using a global perturbation method), there can be contradicting explanations for two data instances for the same class. Even though TCAV is also a type of global perturbation method, unlike LIME, TCAV-generated explanations are not only true for a single data instance but also true for the entire class. This is a major advantage of TCAV over LIME, which increases the user's trust in the explanation method.
- Concept-based explanations...