
The proposed ScoreCAM++ approach enhances the interpretability of deep learning models by modifying the normalization function and incorporating the tanh activation to effectively gate and emphasize the most relevant features in the activation layers.


coremsg

enhancing-visual-explanations-in-convolutional-neural-networks-through-gated-feature-extraction


Enhancing Visual Explanations in Convolutional Neural Networks through Gated Feature Extraction


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ConceptLens enhances the interpretability of deep neural networks by visualizing neuron activations and their associated confidence levels through error-margin analysis, providing insights into how these networks make decisions.


conceptlens-visualizing-neural-network-activations-and-their-confidence-levels-using-error-margin-analysis


ConceptLens: Visualizing Neural Network Activations and Their Confidence Levels Using Error-Margin Analysis
