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 |
---|
Task | Papers | Share |
---|---|---|
Object Detection | 66 | 9.34% |
Semantic Segmentation | 39 | 5.52% |
Image Classification | 34 | 4.81% |
Classification | 29 | 4.10% |
Image Segmentation | 17 | 2.40% |
Self-Supervised Learning | 17 | 2.40% |
Quantization | 13 | 1.84% |
Reinforcement Learning (RL) | 12 | 1.70% |
Autonomous Driving | 8 | 1.13% |
Component | Type |
|
---|---|---|
🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |