Switchable Normalization combines three types of statistics estimated channel-wise, layer-wise, and minibatch-wise by using instance normalization, layer normalization, and batch normalization respectively. Switchable Normalization switches among them by learning their importance weights.
Source: Differentiable Learning-to-Normalize via Switchable NormalizationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
---|---|---|
Pedestrian Detection | 2 | 18.18% |
Retinal Vessel Segmentation | 1 | 9.09% |
Multi-Task Learning | 1 | 9.09% |
Person Re-Identification | 1 | 9.09% |
Person Search | 1 | 9.09% |
Image Generation | 1 | 9.09% |
Ischemic Stroke Lesion Segmentation | 1 | 9.09% |
Lesion Segmentation | 1 | 9.09% |
Semantic Segmentation | 1 | 9.09% |
Component | Type |
|
---|---|---|
Batch Normalization
|
Normalization | |
Instance Normalization
|
Normalization | |
Layer Normalization
|
Normalization | |
Softmax
|
Output Functions |