A 1 x 1 Convolution is a convolution with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an MLP looking at a particular pixel location.
Image Credit: http://deeplearning.ai
Source: Network In NetworkPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 61 | 8.60% |
Semantic Segmentation | 38 | 5.36% |
Image Classification | 33 | 4.65% |
Classification | 28 | 3.95% |
Image Segmentation | 16 | 2.26% |
Quantization | 13 | 1.83% |
Self-Supervised Learning | 11 | 1.55% |
Autonomous Driving | 10 | 1.41% |
Reinforcement Learning (RL) | 10 | 1.41% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |