Papers › Model Zoo: A Growing "Brain" That Learns Continually
Model Zoo: A Growing "Brain" That Learns Continually
Rahul Ramesh, Pratik Chaudhari
This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models. We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them. The generalization error on a particular task can improve when it is trained with synergistic tasks, but can also deteriorate when trained with competing tasks. This theory motivates our method named Model Zoo which, inspired from the boosting literature, grows an ensemble of small models, each of which is trained during one episode of continual learning. We demonstrate that Model Zoo obtains large gains in accuracy on a variety of continual learning benchmark problems. Code is available at https://github.com/grasp-lyrl/modelzoo_continual.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Continual Learning | Cifar100 (20 tasks) | Model Zoo-Continual | Average Accuracy | 94.99 | #1 of 9 | Archive leaderboard | report |
| Continual Learning | Coarse-CIFAR100 | Model Zoo-Continual | Average Accuracy | 84.27 | #1 of 1 | Archive leaderboard | report |
| Continual Learning | Permuted MNIST | Model Zoo-Continual | Average Accuracy | 97.71 | #2 of 3 | Archive leaderboard | report |
| Continual Learning | Rotated MNIST | Model Zoo-Continual | Average Accuracy | 99.66 | #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.
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