Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification

• URL: http://arxiv.org/abs/2405.03301v1
• Date: Mon, 6 May 2024 09:21:35 GMT
• Title: Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
• Authors: Matteo Bianchi, Antonio De Santis, Andrea Tocchetti, Marco Brambilla,
• Abstract summary: State-of-the-art explainability methods generate saliency maps to show where a specific class is identified. We introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. We also show an approach to generate global explanations by aggregating labels across multiple images.
• Score: 5.087579454836169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transparency and explainability in image classification are essential for establishing trust in machine learning models and detecting biases and errors. State-of-the-art explainability methods generate saliency maps to show where a specific class is identified, without providing a detailed explanation of the model's decision process. Striving to address such a need, we introduce a post-hoc method that explains the entire feature extraction process of a Convolutional Neural Network. These explanations include a layer-wise representation of the features the model extracts from the input. Such features are represented as saliency maps generated by clustering and merging similar feature maps, to which we associate a weight derived by generalizing Grad-CAM for the proposed methodology. To further enhance these explanations, we include a set of textual labels collected through a gamified crowdsourcing activity and processed using NLP techniques and Sentence-BERT. Finally, we show an approach to generate global explanations by aggregating labels across multiple images.

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