Papers › DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion

22 Nov 2021CVPR 2022 1arXiv:2111.11326archive 2025-07-28

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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conv1x1 arthurdouillard/dytox/continual/cnn/resnet.py official repository ran · our draft was wrong Apache-2.0 recorded; this copy not marked cleared · pointer only · 2a80220dabcb742a · report
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Tasks

Class Incremental LearningContinual LearningDecoderIncremental Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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