Interpretability Techniques For Deep Learning

Techniques for the explainability of deep learning aim to parse the internal mechanisms of complex neural network models, revealing their decision-making processes and enhancing model transparency and credibility. These techniques help researchers and developers understand model behavior, optimize model performance, and ensure the safety and compliance of models in practical applications by quantifying feature importance, visualizing hidden layer activations, and generating local explanations.