Image Model Blocks
ENet Dilated Bottleneck
Introduced by Paszke et al. in ENet: A Deep Neural Network Architecture for Real-Time Semantic SegmentationENet Dilated Bottleneck is an image model block used in the ENet semantic segmentation architecture. It is the same as a regular ENet Bottleneck but employs dilated convolutions instead.
Source: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 13 | 23.21% |
| Autonomous Driving | 5 | 8.93% |
| Image Segmentation | 2 | 3.57% |
| Quantization | 2 | 3.57% |
| Autonomous Vehicles | 2 | 3.57% |
| Real-Time Semantic Segmentation | 2 | 3.57% |
| Deep Learning | 2 | 3.57% |
| regression | 2 | 3.57% |
| Medical Image Analysis | 2 | 3.57% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
1x1 Convolution
|
Convolutions | |
Batch Normalization
|
Normalization | |
Dilated Convolution
|
Convolutions | |
Max Pooling
|
Pooling Operations | |
PReLU
|
Activation Functions | |
SpatialDropout
|
Regularization |
