Papers › Learning Implicitly Recurrent CNNs Through Parameter Sharing
Learning Implicitly Recurrent CNNs Through Parameter Sharing
Pedro Savarese, Michael Maire
We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates. Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks. Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy. Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops. Training these networks thus implicitly involves discovery of suitable recurrent architectures. Though considering only the design aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures. Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias. Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.
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Code
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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 | CIFAR-10 | Shared WRN | Percentage correct | 97.47 | #80 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | Shared WRN | Percentage correct | 82.57 | #102 of 211 | Archive leaderboard | report |
| Neural Architecture Search | CIFAR-10 | Soft Parameter Sharing | Search Time (GPU days) | 0.7 | #22 of 41 | Archive leaderboard | report |
| Neural Architecture Search | CIFAR-10 | Soft Parameter Sharing | Top-1 Error Rate | 2.53% | #22 of 41 | Archive leaderboard | report |
| Neural Architecture Search | CIFAR-10 Image Classification | Soft Parameter Sharing | Params | 33.5 | #12 of 19 | Archive leaderboard | report |
| Neural Architecture Search | CIFAR-10 Image Classification | Soft Parameter Sharing | Percentage error | 2.53 | #12 of 19 | 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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