Papers › Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, Andrew Zisserman
This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [Zeiler et al., 2013].
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Code
Syntology Ran 1 of 4 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
By repository: community (archive-listed): 3 samples from 2 repositories, 1 ran; 1 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
23 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
4 samples harvested; 1 ran; 0 honoured the contract we drafted; 3 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.
Licence: 1 of the 4 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Attribution | CUB-200-2011 | Saliency | Deletion AUC score (ResNet-101) | 0.0682 | #7 of 8 | Archive leaderboard | report |
| Image Attribution | CUB-200-2011 | Saliency | Insertion AUC score (ResNet-101) | 0.6585 | #7 of 8 | Archive leaderboard | report |
| Image Attribution | CelebA | Saliency | Deletion AUC score (ArcFace ResNet-101) | 0.1453 | #6 of 8 | Archive leaderboard | report |
| Image Attribution | CelebA | Saliency | Insertion AUC score (ArcFace ResNet-101) | 0.4632 | #6 of 8 | Archive leaderboard | report |
| Image Attribution | VGGFace2 | Saliency | Deletion AUC score (ArcFace ResNet-101) | 0.1907 | #6 of 8 | Archive leaderboard | report |
| Image Attribution | VGGFace2 | Saliency | Insertion AUC score (ArcFace ResNet-101) | 0.5612 | #6 of 8 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CelebA | Saliency | Insertion AUC score | 0.4632 | #5 of 7 | 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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