Papers › PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image...
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Giang, Nguyen, Valerie Chen, Mohammad Reza Taesiri, Anh Totti Nguyen
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 image classifier C. We leverage an image comparator S that (1) compares the input image with NN images from the top-K most probable classes given by C; and (2) uses scores from S to weight the confidence scores of C to refine predictions. Our method consistently improves fine-grained image classification accuracy on CUB-200, Cars-196, and Dogs-120. Also, a human study finds that showing users our probable-class nearest neighbors (PCNN) reduces over-reliance on AI, thus improving their decision accuracy over prior work which only shows only the most-probable (top-1) class examples.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | CUB-200-2011 | ResNet-50 | Accuracy | 88.59% | #6 of 12 | Archive leaderboard | report |
| Fine-Grained Image Classification | CUB-200-2011 | ResNet-50 | Accuracy | 88.59 | #20 of 30 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Cars | ResNet-50 | Accuracy | 91.06% | #76 of 83 | Archive leaderboard | report |
| Fine-Grained Image Classification | Stanford Dogs | ResNet-50 | Accuracy | 86.31% | #22 of 24 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: PCNN Ranking
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