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 | 44 | 5.35% |
| Object Detection | 37 | 4.50% |
| Image Segmentation | 26 | 3.16% |
| Image Classification | 23 | 2.80% |
| Decoder | 22 | 2.68% |
| Denoising | 19 | 2.31% |
| Image Generation | 18 | 2.19% |
| Classification | 17 | 2.07% |
| Computational Efficiency | 15 | 1.82% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
