Papers › Essentials for Class Incremental Learning
Essentials for Class Incremental Learning
Sudhanshu Mittal, Silvio Galesso, Thomas Brox
Contemporary neural networks are limited in their ability to learn from evolving streams of training data. When trained sequentially on new or evolving tasks, their accuracy drops sharply, making them unsuitable for many real-world applications. In this work, we shed light on the causes of this well-known yet unsolved phenomenon - often referred to as catastrophic forgetting - in a class-incremental setup. We show that a combination of simple components and a loss that balances intra-task and inter-task learning can already resolve forgetting to the same extent as more complex measures proposed in literature. Moreover, we identify poor quality of the learned representation as another reason for catastrophic forgetting in class-IL. We show that performance is correlated with secondary class information (dark knowledge) learned by the model and it can be improved by an appropriate regularizer. With these lessons learned, class-incremental learning results on CIFAR-100 and ImageNet improve over the state-of-the-art by a large margin, while keeping the approach simple.
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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 | CCIL-SD | Average Incremental Accuracy | 65.86 | #8 of 13 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 5 steps of 10 classes | CCIL-SD | Average Incremental Accuracy | 67.17 | #8 of 15 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 500 classes + 5 steps of 100 classes | CCIL-SD | Average Incremental Accuracy | 68.04 | #2 of 4 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 10 steps of 5 classes | CCIL-SD | Average Incremental Accuracy | 76.77 | #4 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 5 steps of 10 classes | CCIL-SD | Average Incremental Accuracy | 79.44 | #3 of 5 | 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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