Dilated Convolutions are a type of convolution that “inflate” the kernel by inserting holes between the kernel elements. An additional parameter $l$ (dilation rate) indicates how much the kernel is widened. There are usually $l-1$ spaces inserted between kernel elements.
Note that concept has existed in past literature under different names, for instance the algorithme a trous, an algorithm for wavelet decomposition (Holschneider et al., 1987; Shensa, 1992).
Source: Multi-Scale Context Aggregation by Dilated ConvolutionsPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 146 | 15.01% |
Reinforcement Learning (RL) | 77 | 7.91% |
Object Detection | 41 | 4.21% |
Image Segmentation | 41 | 4.21% |
Decoder | 38 | 3.91% |
Continuous Control | 23 | 2.36% |
Autonomous Driving | 21 | 2.16% |
Image Classification | 20 | 2.06% |
Instance Segmentation | 17 | 1.75% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |