Papers › Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
Jang-Hyun Kim, Wonho Choo, Hyun Oh Song
While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks. In this regard, a number of mixup based augmentation methods have been recently proposed. However, these approaches mainly focus on creating previously unseen virtual examples and can sometimes provide misleading supervisory signal to the network. To this end, we propose Puzzle Mix, a mixup method for explicitly utilizing the saliency information and the underlying statistics of the natural examples. This leads to an interesting optimization problem alternating between the multi-label objective for optimal mixing mask and saliency discounted optimal transport objective. Our experiments show Puzzle Mix achieves the state of the art generalization and the adversarial robustness results compared to other mixup methods on CIFAR-100, Tiny-ImageNet, and ImageNet datasets. The source code is available at https://github.com/snu-mllab/PuzzleMix.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-100 | WRN28-10 | Percentage correct | 84.05 | #79 of 211 | Archive leaderboard | report |
| Image Classification | ImageNet | ResNet-50 | Top 1 Accuracy | 78.76% | #808 of 1060 | Archive leaderboard | report |
| Image Classification | Tiny-ImageNet | PreActResNet18 | Top 1 Accuracy | 63.48 | #2 of 4 | Archive leaderboard | report |
| Semantic Segmentation | ACDC Scribbles | Puzzle Mix | Dice (Average) | 62.4% | #6 of 6 | 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
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