Papers › FOSTER: Feature Boosting and Compression for Class-Incremental Learning
FOSTER: Feature Boosting and Compression for Class-Incremental Learning
Fu-Yun Wang, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
The ability to learn new concepts continually is necessary in this ever-changing world. However, deep neural networks suffer from catastrophic forgetting when learning new categories. Many works have been proposed to alleviate this phenomenon, whereas most of them either fall into the stability-plasticity dilemma or take too much computation or storage overhead. Inspired by the gradient boosting algorithm to gradually fit the residuals between the target model and the previous ensemble model, we propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively. Specifically, we first dynamically expand new modules to fit the residuals between the target and the output of the original model. Next, we remove redundant parameters and feature dimensions through an effective distillation strategy to maintain the single backbone model. We validate our method FOSTER on CIFAR-100 and ImageNet-100/1000 under different settings. Experimental results show that our method achieves state-of-the-art performance. Code is available at: https://github.com/G-U-N/ECCV22-FOSTER.
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Code
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Code Syntology ran Syntology
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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 | FOSTER | Average Incremental Accuracy | 67.95 | #5 of 13 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 25 steps of 2 classes | FOSTER | Average Incremental Accuracy | 63.83 | #3 of 5 | Archive leaderboard | report |
| Incremental Learning | CIFAR-100 - 50 classes + 5 steps of 10 classes | FOSTER | Average Incremental Accuracy | 69.46 | #5 of 15 | Archive leaderboard | report |
| Incremental Learning | CIFAR100-B0(10steps of 10 classes) | FOSTER | Average Incremental Accuracy | 72.9 | #5 of 6 | Archive leaderboard | report |
| Incremental Learning | CIFAR100B020Step(5ClassesPerStep) | FOSTER | Average Incremental Accuracy | 70.65 | #5 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 10 steps | FOSTER | Average Incremental Accuracy | 68.34 | #4 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 10 steps of 5 classes | FOSTER | Average Incremental Accuracy | 77.54 | #3 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 25 steps of 2 classes | FOSTER | Average Incremental Accuracy | 69.34 | #2 of 3 | Archive leaderboard | report |
| Incremental Learning | ImageNet-100 - 50 classes + 5 steps of 10 classes | FOSTER | Average Incremental Accuracy | 80.22 | #1 of 5 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | FOSTER | Average Incremental Accuracy | 77.75 | #3 of 13 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 20 steps | FOSTER | Average Incremental Accuracy | 74.49 | #1 of 1 | 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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