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insight - Explainable AI for Convolutional Neural Networks
Enhancing Visual Explanations in Convolutional Neural Networks through Gated Feature Extraction
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.
ConceptLens: Visualizing Neural Network Activations and Their Confidence Levels Using Error-Margin Analysis
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.
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