An important research effort has been recently dedicated to understand the decision mechanism of deep neural networks. Among them, Class Activation Mapping (CAM) and its variations have proved their capacity to obtain useful insights about Convolutional Neural Network (CNN) models’ decisions. However, these methods remain limited to the supervised case regardless of CNN-based advances in unsupervised tasks such as clustering. To fill this gap, we propose a new method called Grad-CeAM for centroid-based clustering methods used on CNN representation. Through an experimental study, we show that our method has the capacity to localize discriminating features used by a CNN model to create its representation and that it can be used to explain the clusters assignment. We also show that this method can be used in different application domains by providing uses cases on time series and images clustering.
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