Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes
arxiv(2021)
摘要
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, yielding
explanations in the form of "this looks like that." However, while previous
methods use spatially rigid prototypes, we address this shortcoming by
proposing spatially flexible prototypes. Each prototype is made up of several
prototypical parts that adaptively change their relative spatial positions
depending on the input image. Consequently, a Deformable ProtoPNet can
explicitly capture pose variations and context, improving both model accuracy
and the richness of explanations provided. Compared to other case-based
interpretable models using prototypes, our approach achieves state-of-the-art
accuracy and gives an explanation with greater context. The code is available
at https://github.com/jdonnelly36/Deformable-ProtoPNet.
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