[D] Are there no proper explainability approaches for semantic segmentation models?

Hey there, I'm currently looking more into explainability in AI and would like to get more insights to the results of my segmentation models (U-Net and DeepLabV3).

Looking for possibilities to explain my outputs (or intermediate layers of my networks), I couldn't really find solid results w.r.t. segmentation tasks.

I could see that in the SHAP examples, there is one showing how to make some explanations on an intermediate layer of VGG16 on ImageNet with PyTorch ([here](https://shap.readthedocs.io/en/latest/example_notebooks/image_examples/image_classification/Explain%20an%20Intermediate%20Layer%20of%20VGG16%20on%20ImageNet%20%28PyTorch%29.html)). This, however, still …

deeplabv3 explainability hey insights intermediate machinelearning networks results segmentation semantic solid tasks