Convolutions
Dilated Convolution
Introduced by Yu et al. in Multi-Scale Context Aggregation by Dilated ConvolutionsDilated 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 Convolutions
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 134 | 12.42% |
| Reinforcement Learning (RL) | 80 | 7.41% |
| Deep Reinforcement Learning | 55 | 5.10% |
| Reinforcement Learning | 49 | 4.54% |
| Object Detection | 40 | 3.71% |
| Image Segmentation | 38 | 3.52% |
| Decoder | 32 | 2.97% |
| Continuous Control | 27 | 2.50% |
| Object | 19 | 1.76% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
