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 |
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Task | Papers | Share |
---|---|---|
Image Classification | 101 | 14.41% |
Object Detection | 49 | 6.99% |
Semantic Segmentation | 37 | 5.28% |
Classification | 34 | 4.85% |
General Classification | 29 | 4.14% |
Instance Segmentation | 16 | 2.28% |
Test | 14 | 2.00% |
Quantization | 11 | 1.57% |
Multi-Task Learning | 10 | 1.43% |
Component | Type |
|
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Pooling Operations | |
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Convolutions | |
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Feedforward Networks | |
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Activation Functions | |
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Activation Functions |