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 | 43 | 5.28% |
| Object Detection | 31 | 3.81% |
| Image Segmentation | 22 | 2.70% |
| Image Classification | 22 | 2.70% |
| Denoising | 21 | 2.58% |
| Decoder | 20 | 2.46% |
| Computational Efficiency | 17 | 2.09% |
| Image Generation | 15 | 1.84% |
| Classification | 13 | 1.60% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
