Papers › FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
David W. Romero, Robert-Jan Bruintjes, Jakub M. Tomczak, Erik J. Bekkers, Mark Hoogendoorn, Jan C. van Gemert
When designing Convolutional Neural Networks (CNNs), one must select the size\break of the convolutional kernels before training. Recent works show CNNs benefit from different kernel sizes at different layers, but exploring all possible combinations is unfeasible in practice. A more efficient approach is to learn the kernel size during training. However, existing works that learn the kernel size have a limited bandwidth. These approaches scale kernels by dilation, and thus the detail they can describe is limited. In this work, we propose FlexConv, a novel convolutional operation with which high bandwidth convolutional kernels of learnable kernel size can be learned at a fixed parameter cost. FlexNets model long-term dependencies without the use of pooling, achieve state-of-the-art performance on several sequential datasets, outperform recent works with learned kernel sizes, and are competitive with much deeper ResNets on image benchmark datasets. Additionally, FlexNets can be deployed at higher resolutions than those seen during training. To avoid aliasing, we propose a novel kernel parameterization with which the frequency of the kernels can be analytically controlled. Our novel kernel parameterization shows higher descriptive power and faster convergence speed than existing parameterizations. This leads to important improvements in classification accuracy.
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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 | FlexTCN-7 | Percentage correct | 92.2 | #184 of 265 | Archive leaderboard | report |
| Sequential Image Classification | Sequential CIFAR-10 | FlexTCN-6 | Unpermuted Accuracy | 80.82% | #6 of 13 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | FlexTCN-4 | Permuted Accuracy | 98.72% | #3 of 30 | Archive leaderboard | report |
| Sequential Image Classification | Sequential MNIST | FlexTCN-6 | Unpermuted Accuracy | 99.62% | #28 of 30 | Archive leaderboard | report |
| Sequential Image Classification | noise padded CIFAR-10 | FlexTCN-6 | % Test Accuracy | 69.87% | #1 of 7 | Archive leaderboard | report |
| Time Series Analysis | Speech Commands | FlexTCN-4 | % Test Accuracy | 97.73 | #3 of 6 | Archive leaderboard | report |
| Time Series Analysis | Speech Commands | FlexTCN-6 | % Test Accuracy (Raw Data) | 91.73 | #6 of 6 | 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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