Convolutions
Dynamic Convolution
Introduced by Wu et al. in Pay Less Attention with Lightweight and Dynamic ConvolutionsDynamicConv is a type of convolution for sequential modelling where it has kernels that vary over time as a learned function of the individual time steps. It builds upon LightConv and takes the same form but uses a time-step dependent kernel:
$$ \text{DynamicConv}\left(X, i, c\right) = \text{LightConv}\left(X, f\left(X_{i}\right)_{h,:}, i, c\right) $$
Source: Pay Less Attention with Lightweight and Dynamic Convolutions
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Machine Translation | 4 | 16.67% |
| Translation | 4 | 16.67% |
| Object Detection | 2 | 8.33% |
| Bias Detection | 1 | 4.17% |
| RGB Salient Object Detection | 1 | 4.17% |
| Salient Object Detection | 1 | 4.17% |
| Automatic Speech Recognition (ASR) | 1 | 4.17% |
| Punctuation Restoration | 1 | 4.17% |
| Speech Recognition | 1 | 4.17% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
