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Normalization operations #10

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opened 2024-01-03 14:36:56 +00:00 by julius · 0 comments
julius commented 2024-01-03 14:36:56 +00:00
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Methods

  • Min-Max Normalization: Scale data to a fixed range (0 to 1).
  • Z-Score Normalization (Standardization): Transform data to have a mean of 0 and standard deviation of 1.
  • L1 Normalization: Scale so the sum of absolute values is 1.
  • L2 Normalization: Scale so the sum of squares is 1.
  • Max Normalization: Divide each element by the maximum absolute value.
  • Batch Normalization: Normalize using batch mean and standard deviation (in neural networks).
  • Layer Normalization: Normalize across each feature.
  • Instance Normalization: Normalize each instance independently, used in style transfer.
  • Group Normalization: Divide channels into groups and normalize separately.
  • Contrast Normalization: Adjust the contrast of images by normalizing pixel values.
## Methods - [ ] Min-Max Normalization: Scale data to a fixed range (0 to 1). - [ ] Z-Score Normalization (Standardization): Transform data to have a mean of 0 and standard deviation of 1. - [ ] L1 Normalization: Scale so the sum of absolute values is 1. - [ ] L2 Normalization: Scale so the sum of squares is 1. - [ ] Max Normalization: Divide each element by the maximum absolute value. - [ ] Batch Normalization: Normalize using batch mean and standard deviation (in neural networks). - [ ] Layer Normalization: Normalize across each feature. - [ ] Instance Normalization: Normalize each instance independently, used in style transfer. - [ ] Group Normalization: Divide channels into groups and normalize separately. - [ ] Contrast Normalization: Adjust the contrast of images by normalizing pixel values.
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julius removed the
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julius added this to the manifold project 2024-01-06 00:28:44 +00:00
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Reference: julius/manifold#10
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