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 | 46 | 5.75% |
| Object Detection | 40 | 5.00% |
| Image Classification | 25 | 3.13% |
| Image Segmentation | 24 | 3.00% |
| Denoising | 23 | 2.88% |
| Image Generation | 22 | 2.75% |
| Decoder | 20 | 2.50% |
| Classification | 15 | 1.88% |
| Self-Supervised Learning | 11 | 1.38% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
