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Alina Jade Barnett

2 accepted papers

2025

Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time

CVPR 2025poster

Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part models (ProtoPNets) have become the dominant model type to meet this need. Users can easily identify flaws in ProtoPNets, bu…

2022

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes

CVPR 2022poster

We present a deformable prototypical part network (Deformable ProtoPNet), an interpretable image classifier that integrates the power of deep learning and the interpretability of case-based reasoning. This model classifies input images by comparing them with prototypes learned during training, yield…

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