While standard convolution performs the channelwise and spatial-wise computation in one step, Depthwise Separable Convolution splits the computation into two steps: depthwise convolution applies a single convolutional filter per each input channel and pointwise convolution is used to create a linear combination of the output of the depthwise convolution. The comparison of standard convolution and depthwise separable convolution is shown to the right.
Credit: Depthwise Convolution Is All You Need for Learning Multiple Visual Domains
Source: Xception: Deep Learning With Depthwise Separable ConvolutionsPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Image Classification | 87 | 12.24% |
Object Detection | 59 | 8.30% |
Classification | 37 | 5.20% |
Semantic Segmentation | 34 | 4.78% |
Quantization | 33 | 4.64% |
General Classification | 24 | 3.38% |
Model Compression | 12 | 1.69% |
Knowledge Distillation | 10 | 1.41% |
Multi-Task Learning | 10 | 1.41% |