The Squeeze-and-Excitation Block is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. The process is:
Paper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Image Classification | 104 | 13.72% |
Object Detection | 48 | 6.33% |
Semantic Segmentation | 38 | 5.01% |
Classification | 37 | 4.88% |
General Classification | 29 | 3.83% |
Instance Segmentation | 17 | 2.24% |
Decoder | 16 | 2.11% |
Quantization | 13 | 1.72% |
Multi-Task Learning | 10 | 1.32% |
Component | Type |
|
---|---|---|
Average Pooling
|
Pooling Operations | |
Convolution
|
Convolutions | |
Dense Connections
|
Feedforward Networks | |
ReLU
|
Activation Functions | |
Sigmoid Activation
|
Activation Functions |