PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
arxiv(2023)
摘要
Nearest neighbors (NN) are traditionally used to compute final decisions,
e.g., in Support Vector Machines or k-NN classifiers, and to provide users with
explanations for the model's decision. In this paper, we show a novel utility
of nearest neighbors: To improve predictions of a frozen, pretrained classifier
C. We leverage an image comparator S that (1) compares the input image with NN
images from the top-K most probable classes; and (2) uses S's output scores to
weight the confidence scores of C. Our method consistently improves
fine-grained image classification accuracy on CUB-200, Cars-196, and Dogs-120.
Also, a human study finds that showing lay users our probable-class nearest
neighbors (PCNN) improves their decision accuracy over prior work which only
shows only the top-1 class examples.
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