Visual Explanation via Similar Feature Activation for Metric Learning

The introduction of the Similar Feature Activation Map (SFAM) marks a significant advancement in the field of deep learning, particularly for metric learning models that traditionally lack the fully connected layers required for conventional visual explanation methods. SFAM utilizes a channel-wise contribution importance score (CIS) to assess feature importance based on the similarity between image embeddings, thus providing a novel approach to generating interpretable visual explanations. Quantitative and qualitative experiments have demonstrated that SFAM offers promising results, enhancing the trustworthiness of decisions made by convolutional neural networks (CNNs). This development is crucial as it not only improves the interpretability of AI systems but also guides the creation of new algorithms in image recognition tasks, addressing a critical gap in the current methodologies.