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 | 60 | 9.08% |
Semantic Segmentation | 39 | 5.90% |
Image Classification | 34 | 5.14% |
Classification | 28 | 4.24% |
Self-Supervised Learning | 19 | 2.87% |
Quantization | 15 | 2.27% |
Image Segmentation | 15 | 2.27% |
Reinforcement Learning (RL) | 12 | 1.82% |
Autonomous Driving | 10 | 1.51% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |