Skip Connections
Concatenated Skip Connection
A Concatenated Skip Connection is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 97 | 10.84% |
| Image Segmentation | 66 | 7.37% |
| Decoder | 54 | 6.03% |
| Denoising | 53 | 5.92% |
| Image Generation | 42 | 4.69% |
| Medical Image Segmentation | 40 | 4.47% |
| Tumor Segmentation | 18 | 2.01% |
| Classification | 13 | 1.45% |
| Image Classification | 13 | 1.45% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
