Image Model Blocks
Efficient Spatial Pyramid
Introduced by Mehta et al. in ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic SegmentationAn Efficient Spatial Pyramid (ESP) is an image model block based on a factorization principle that decomposes a standard convolution into two steps: (1) point-wise convolutions and (2) spatial pyramid of dilated convolutions. The point-wise convolutions help in reducing the computation, while the spatial pyramid of dilated convolutions re-samples the feature maps to learn the representations from large effective receptive field. This allows for increased efficiency compared to another image blocks like ResNeXt blocks and Inception modules.
Source: ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Speech Recognition | 10 | 21.74% |
| Automatic Speech Recognition | 8 | 17.39% |
| Semantic Segmentation | 5 | 10.87% |
| reinforcement Learning | 3 | 6.52% |
| Speech Separation | 2 | 4.35% |
| Language Modelling | 2 | 4.35% |
| Real-Time Semantic Segmentation | 2 | 4.35% |
| Robust Speech Recognition | 1 | 2.17% |
| Speech Enhancement | 1 | 2.17% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
Dilated Convolution
|
Convolutions | |
Hierarchical Feature Fusion
|
Degridding | |
Pointwise Convolution
|
Convolutions |
