Preeti
In the world of Machine Learning, new methods and tools are constantly being developed for public use. Understanding models is crucial for transparency and debugging neural networks. There are three main types of model interpretability methods: gradient-based, perturbation-based, and SmoothGrad-based. Python offers various libraries for interpretation and explainability, with Captum being a powerful tool developed by Facebook AI Research.
Integrated Gradients compute the integral of gradients along a path to assign importance scores based on feature variations. DeepLift compares input activations to reference activations to compute feature importance scores. Guided GradCAM combines GradCAM and guided backpropagation to visualize important image regions. Saliency methods analyze and quantify the importance of elements in images.
Other techniques like LRP distribute relevance back through network layers, Input X Gradient combines gradients with input features, and SHAP methods explain predictions using Shapley values. Feature ablation and permutation assess feature importance, while LIME provides local explanations for model predictions. Occlusion and Shapely Value Sampling are used to identify critical regions and estimate Shapley values efficiently.
SmoothGrad, VarGrad, and SmoothGrad Square are variations that enhance the interpretability and stability of feature attribution methods in deep learning models. These techniques aim to reduce noise, capture uncertainty, and emphasize important features for better visualization and understanding of model decisions.
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