Papers › DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion
Arthur Douillard, Alexandre Ramé, Guillaume Couairon, Matthieu Cord
Deep network architectures struggle to continually learn new tasks without forgetting the previous tasks. A recent trend indicates that dynamic architectures based on an expansion of the parameters can reduce catastrophic forgetting efficiently in continual learning. However, existing approaches often require a task identifier at test-time, need complex tuning to balance the growing number of parameters, and barely share any information across tasks. As a result, they struggle to scale to a large number of tasks without significant overhead. In this paper, we propose a transformer architecture based on a dedicated encoder/decoder framework. Critically, the encoder and decoder are shared among all tasks. Through a dynamic expansion of special tokens, we specialize each forward of our decoder network on a task distribution. Our strategy scales to a large number of tasks while having negligible memory and time overheads due to strict control of the parameters expansion. Moreover, this efficient strategy doesn't need any hyperparameter tuning to control the network's expansion. Our model reaches excellent results on CIFAR100 and state-of-the-art performances on the large-scale ImageNet100 and ImageNet1000 while having less parameters than concurrent dynamic frameworks.
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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 | ImageNet - 10 steps | DyTox | # M Params | 11.36 | #2 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 10 steps | DyTox | Average Incremental Accuracy | 71.29 | #2 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 10 steps | DyTox | Average Incremental Accuracy Top-5 | 88.59 | #2 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 10 steps | DyTox | Final Accuracy | 63.34 | #2 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet - 10 steps | DyTox | Final Accuracy Top-5 | 84.49 | #2 of 10 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | DyTox | # M Params | 11.01 | #7 of 13 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | DyTox | Average Incremental Accuracy | 77.15 | #7 of 13 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | DyTox | Average Incremental Accuracy Top-5 | 92.04 | #7 of 13 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | DyTox | Final Accuracy | 69.10 | #7 of 13 | Archive leaderboard | report |
| Incremental Learning | ImageNet100 - 10 steps | DyTox | Final Accuracy Top-5 | 87.98 | #7 of 13 | 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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