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 | 5.87% |
| Object Detection | 45 | 5.62% |
| Image Classification | 28 | 3.50% |
| Denoising | 23 | 2.87% |
| Image Generation | 23 | 2.87% |
| Image Segmentation | 21 | 2.62% |
| Classification | 16 | 2.00% |
| Self-Supervised Learning | 12 | 1.50% |
| Computational Efficiency | 11 | 1.37% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
