Methods › Sequential › Temporal Convolutions
Temporal Convolutions
Convolutions are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.
Methods
All 7 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| Dilated Causal Convolution | – | 193 |
| Gated Convolution | – | 35 |
| Causal Convolution | – | 27 |
| ConvTasNet Convolutional time-domain audio separation network | – | 11 |
| DynamicConv Dynamic Convolution | – | 11 |
| Span-Based Dynamic Convolution | – | 6 |
| TaLK Convolution Time-aware Large Kernel Convolution | – | 1 |