Abstract:Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data are scarce and imbalanced. As these applications make high-stake decisions and involve societal values like fairness and transparency, it is critical to explain the learned models. We aim to study post-hoc explanations of Siamese networks (SN) widely used in learning to compare. We characterize the instability of gradientbased explanations due to t… Show more
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