Skip Connection Blocks
SqueezeNeXt Block
Introduced by Gholami et al. in SqueezeNext: Hardware-Aware Neural Network DesignA SqueezeNeXt Block is a two-stage bottleneck module used in the SqueezeNeXt architecture to reduce the number of input channels to the 3 × 3 convolution. We decompose with separable convolutions to further reduce the number of parameters (orange parts), followed by a 1 × 1 expansion module.
Source: SqueezeNext: Hardware-Aware Neural Network Design
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Quantization | 1 | 33.33% |
| Clustering Multivariate Time Series | 1 | 33.33% |
| Multivariate Time Series Imputation | 1 | 33.33% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
1x1 Convolution
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Convolutions | |
Convolution
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Convolutions | |
Residual Connection
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Skip Connections | |
Spatially Separable Convolution
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Convolutions |
