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 |
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Task | Papers | Share |
---|---|---|
Image Classification | 69 | 8.60% |
Object Detection | 42 | 5.24% |
Classification | 37 | 4.61% |
Deep Learning | 36 | 4.49% |
Quantization | 28 | 3.49% |
Semantic Segmentation | 26 | 3.24% |
Computational Efficiency | 16 | 2.00% |
Management | 15 | 1.87% |
Ensemble Learning | 14 | 1.75% |