Convolutions
Convolution
A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.
Intuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).
Image Source: https://arxiv.org/pdf/1603.07285.pdf
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 47 | 5.82% |
| Object Detection | 41 | 5.08% |
| Image Segmentation | 25 | 3.10% |
| Image Classification | 23 | 2.85% |
| Denoising | 23 | 2.85% |
| Image Generation | 22 | 2.73% |
| Decoder | 21 | 2.60% |
| Classification | 15 | 1.86% |
| Medical Image Segmentation | 11 | 1.36% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
