Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification
<|reference_start|>Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification: 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.<|reference_end|>
@article{bianchi2024interpretable,
    title={Interpretable Network Visualizations: A Human-in-the-Loop Approach for
  Post-hoc Explainability of CNN-based Image Classification},
    author={Matteo Bianchi, Antonio De Santis, Andrea Tocchetti and Marco
  Brambilla},
    journal={IJCAI 2024},
    year={2024},
    doi={10.24963/ijcai.2024/411},
    archivePrefix={arXiv},
    eprint={2405.03301},
    primaryClass={cs.LG cs.CV}
}