Papers › Domain Generalization by Solving Jigsaw Puzzles
Domain Generalization by Solving Jigsaw Puzzles
Fabio Maria Carlucci, Antonio D'Innocente, Silvia Bucci, Barbara Caputo, Tatiana Tommasi
Human adaptability relies crucially on the ability to learn and merge knowledge both from supervised and unsupervised learning: the parents point out few important concepts, but then the children fill in the gaps on their own. This is particularly effective, because supervised learning can never be exhaustive and thus learning autonomously allows to discover invariances and regularities that help to generalize. In this paper we propose to apply a similar approach to the task of object recognition across domains: our model learns the semantic labels in a supervised fashion, and broadens its understanding of the data by learning from self-supervised signals how to solve a jigsaw puzzle on the same images. This secondary task helps the network to learn the concepts of spatial correlation while acting as a regularizer for the classification task. Multiple experiments on the PACS, VLCS, Office-Home and digits datasets confirm our intuition and show that this simple method outperforms previous domain generalization and adaptation solutions. An ablation study further illustrates the inner workings of our approach.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Generalization | NICO Animal | JiGen (Resnet-18) | Accuracy | 84.95 | #3 of 5 | Archive leaderboard | report |
| Domain Generalization | NICO Vehicle | ResNet-18 | Accuracy | 77.39 | #4 of 5 | Archive leaderboard | report |
| Domain Generalization | PACS | JiGen (Resnet-18) | Average Accuracy | 80.51 | #94 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | Deep All (Resnet-18) | Average Accuracy | 79.05 | #98 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | JiGen (Alexnet) | Average Accuracy | 73.38 | #110 of 133 | Archive leaderboard | report |
| Domain Generalization | PACS | Deep All (Alexnet) | Average Accuracy | 71.52 | #117 of 133 | Archive leaderboard | report |
| Image Classification | Colored-MNIST(with spurious correlation) | JiGen | Accuracy | 11.91 | #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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