Convolutions
1x1 Convolution
Introduced by Lin et al. in Network In NetworkA 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an MLP looking at a particular pixel location.
Image Credit: http://deeplearning.ai
Source: Network In Network
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 59 | 8.68% |
| Image Classification | 44 | 6.47% |
| Semantic Segmentation | 39 | 5.74% |
| Self-Supervised Learning | 30 | 4.41% |
| Classification | 30 | 4.41% |
| Quantization | 16 | 2.35% |
| Autonomous Driving | 11 | 1.62% |
| Image Segmentation | 10 | 1.47% |
| Instance Segmentation | 9 | 1.32% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
