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 | 52 | 7.53% |
Object Detection | 40 | 5.79% |
Image Segmentation | 25 | 3.62% |
Image Generation | 22 | 3.18% |
Image Classification | 21 | 3.04% |
Denoising | 16 | 2.32% |
Super-Resolution | 14 | 2.03% |
Classification | 11 | 1.59% |
Self-Supervised Learning | 11 | 1.59% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |