Papers › Multiscale Deep Equilibrium Models
Multiscale Deep Equilibrium Models
Shaojie Bai, Vladlen Koltun, J. Zico Kolter
We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. An MDEQ directly solves for and backpropagates through the equilibrium points of multiple feature resolutions simultaneously, using implicit differentiation to avoid storing intermediate states (and thus requiring only O(1) memory consumption). These simultaneously-learned multi-resolution features allow us to train a single model on a diverse set of tasks and loss functions, such as using a single MDEQ to perform both image classification and semantic segmentation. We illustrate the effectiveness of this approach on two large-scale vision tasks: ImageNet classification and semantic segmentation on high-resolution images from the Cityscapes dataset. In both settings, MDEQs are able to match or exceed the performance of recent competitive computer vision models: the first time such performance and scale have been achieved by an implicit deep learning approach. The code and pre-trained models are at https://github.com/locuslab/mdeq .
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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 |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | Multiscale DEQ (MDEQ-XL) | Number of params | 81M | #771 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Multiscale DEQ (MDEQ-XL) | Top 1 Accuracy | 79.2% | #771 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | Multiscale DEQ (MDEQ-XL) | mIoU | 80.3% | #54 of 99 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | Multiscale DEQ (MDEQ-large) | mIoU | 77.8% | #63 of 99 | 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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