Methods › Computer Vision › Convolutions › DCLS
Dilated convolution with learnable spacings
DCLS
Introduced by Ismail Khalfaoui-Hassani et al. in Dilated convolution with learnable spacings
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Dilated convolution with learnable spacings (DCLS) is a type of convolution that allows the spacings between the non-zero elements of the kernel to be learned during training. This makes it possible to increase the receptive field of the convolution without increasing the number of parameters, which can improve the performance of the network on tasks that require long-range dependencies.
A dilated convolution is a type of convolution that allows the kernel to be skipped over some of the input features. This is done by inserting zeros between the non-zero elements of the kernel. The effect of this is to increase the receptive field of the convolution without increasing the number of parameters.
DCLS takes this idea one step further by allowing the spacings between the non-zero elements of the kernel to be learned during training. This means that the network can learn to skip over different input features depending on the task at hand. This can be particularly helpful for tasks that require long-range dependencies, such as image segmentation and object detection.
DCLS has been shown to be effective for a variety of tasks, including image classification, object detection, and semantic segmentation. It is a promising new technique that has the potential to improve the performance of convolutional neural networks on a variety of tasks.
Papers archive 2025-07-28
6 shown of 6, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Dilated Convolution with Learnable Spacings 10 Aug 2024 · 0 repositories · arXiv:2408.06383
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Dilated Convolution with Learnable Spacings makes visual models more aligned with humans: a Grad-CAM study 6 Aug 2024 · 1 repository · arXiv:2408.03164
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Audio classification with Dilated Convolution with Learnable Spacings 25 Sep 2023 · 2 repositories · arXiv:2309.13972
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Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings 30 Jun 2023 · 1 repository · arXiv:2306.17670
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Dilated Convolution with Learnable Spacings: beyond bilinear interpolation 1 Jun 2023 · 1 repository · arXiv:2306.00817
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Dilated convolution with learnable spacings 7 Dec 2021 · 3 repositories · arXiv:2112.03740Syntology ran 0 of 3 samples · 3 unverified
Tasks archive 2025-07-28
9 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Audio Classification | 3 |
| Object Detection | 2 |
| Semantic Segmentation | 2 |
| Audio Tagging | 1 |
| Classification | 1 |
| Image Classification | 1 |
| Speech Recognition | 1 |
| object-detection | 1 |
| speech-recognition | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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