Papers › PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, Eduardo Valle
Lifelong learning has attracted much attention, but existing works still struggle to fight catastrophic forgetting and accumulate knowledge over long stretches of incremental learning. In this work, we propose PODNet, a model inspired by representation learning. By carefully balancing the compromise between remembering the old classes and learning new ones, PODNet fights catastrophic forgetting, even over very long runs of small incremental tasks --a setting so far unexplored by current works. PODNet innovates on existing art with an efficient spatial-based distillation-loss applied throughout the model and a representation comprising multiple proxy vectors for each class. We validate those innovations thoroughly, comparing PODNet with three state-of-the-art models on three datasets: CIFAR100, ImageNet100, and ImageNet1000. Our results showcase a significant advantage of PODNet over existing art, with accuracy gains of 12.10, 6.51, and 2.85 percentage points, respectively. Code is available at https://github.com/arthurdouillard/incremental_learning.pytorch
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Incremental Learning | CIFAR-100 - 50 classes + 10 steps of 5 classes | PODNet (CNN) | Average Incremental Accuracy | 63.19 | #9 of 13 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 25 steps of 2 classes | PODNet | Average Incremental Accuracy | 60.72 | #4 of 5 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 5 steps of 10 classes | PODNet (CNN) | Average Incremental Accuracy | 64.83 | #9 of 15 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 50 steps of 1 class | PODNet | Average Incremental Accuracy | 57.98 | #1 of 2 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100-B0(5steps of 20 classes) | PODNet | Average Incremental Accuracy | 66.70 | #9 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 500 classes + 10 steps of 50 classes | PODNet | Average Incremental Accuracy | 64.13 | #2 of 4 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 500 classes + 5 steps of 100 classes | PODNet | Average Incremental Accuracy | 66.95 | #3 of 4 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 10 steps of 5 classes | PODNet | Average Incremental Accuracy | 73.14 | #5 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 25 steps of 2 classes | PODNet | Average Incremental Accuracy | 67.28 | #3 of 3 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 5 steps of 10 classes | PODNet | Average Incremental Accuracy | 75.82 | #4 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 50 steps of 1 class | PODNet | Average Incremental Accuracy | 62.08 | #1 of 2 | 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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