Papers › Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

3 Oct 2019arXiv:1910.01279archive 2025-07-28

Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance and fairness for interpreting the decision making process. Our approach outperforms previous methods on both recognition and localization tasks, it also passes the sanity check. We also indicate its application as debugging tools. Official code has been released.

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Syntology Ran 3 of 13 code samples harvested from 4 repositories linked to this paper; 10 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 2 ran · fixture could not drive it.

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haofanwang/Score-CAM officialmentioned in papermentioned on GitHubpytorchMIT report
Jupetus/ExplainableAI mentioned on GitHubpytorch report
andreysorokin/scam-net mentioned on GitHubApache-2.0 report
frgfm/torch-cam mentioned on GitHubpytorch report
jacobgil/pytorch-grad-cam mentioned on GitHubpytorch report
matheushent/score-cam mentioned on GitHubtfMIT report
tabayashi0117/Score-CAM mentioned on GitHubtf report
windstormer/Cfd-CAM mentioned on GitHubpytorch report
yiskw713/scorecam mentioned on GitHubpytorch report

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13 samples harvested; 3 ran; 0 honoured the contract we drafted; 10 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
2ran · fixture could not drive it
10unverified

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energy_point_game haofanwang/Score-CAM/utils/energyPointGame.py official repository unverified MIT (permissive) · 0eb6b543eedbb913 · report
masked_inputs tabayashi0117/Score-CAM/scorecam/score_cam.py community (archive-listed) ran · fixture could not drive it MIT (permissive) · d3ba787775c076f1 · report
ScoreCam tabayashi0117/Score-CAM/scorecam/score_cam.py community (archive-listed) unverified MIT (permissive) · 8abe77c90e3bab0d · report
grid_display matheushent/score-cam/utils/display.py community (archive-listed) unverified MIT (permissive) · a6751790d12dd02d · report
heatmap_display matheushent/score-cam/utils/display.py community (archive-listed) unverified MIT (permissive) · c54afa1ecb39fd2f · report
image_to_uint_255 matheushent/score-cam/utils/display.py community (archive-listed) unverified MIT (permissive) · 1ea162efebe64eb5 · report
normalize_activations andreysorokin/scam-net/scam/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 1b3ec8aff736ee84 · report
normalize_activations matheushent/score-cam/utils/image.py community (archive-listed) unverified MIT (permissive) · 5444b8777b09ceb1 · report
resize_activations andreysorokin/scam-net/scam/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 0092b2f7ee2b970b · report
resize_activations matheushent/score-cam/utils/image.py community (archive-listed) unverified MIT (permissive) · f2f03a34ff27740f · report
upsample_and_normalise tabayashi0117/Score-CAM/scorecam/score_cam.py community (archive-listed) unverified MIT (permissive) · a658de56603d3a9a · report
swin_reshape_transform identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · cd9e431963bd28ca · report
vit_reshape_transform identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 3e3e7c2d436361ca · report

Tasks

Adversarial AttackDecision MakingError UnderstandingFairness

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Error Understanding CUB-200-2011 Score-CAM Average highest confidence (EfficientNetV2-M) 0.2403 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 Score-CAM Average highest confidence (MobileNetV2) 0.3141 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 Score-CAM Average highest confidence (ResNet-101) 0.2510 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 Score-CAM Insertion AUC score (EfficientNetV2-M) 0.1572 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 Score-CAM Insertion AUC score (MobileNetV2) 0.1195 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 Score-CAM Insertion AUC score (ResNet-101) 0.1073 #3 of 4 Archive leaderboard report
Error Understanding CUB-200-2011 (ResNet-101) Score-CAM Average highest confidence 0.2510 #2 of 3 Archive leaderboard report
Error Understanding CUB-200-2011 (ResNet-101) Score-CAM Insertion AUC score 0.1073 #2 of 3 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.

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