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
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Semantic Segmentation | 46 | 6.34% |
Object Detection | 41 | 5.66% |
Image Generation | 26 | 3.59% |
Image Classification | 24 | 3.31% |
Image Segmentation | 23 | 3.17% |
Denoising | 18 | 2.48% |
Classification | 14 | 1.93% |
Super-Resolution | 13 | 1.79% |
Self-Supervised Learning | 11 | 1.52% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |