Body: <p>Which hyper-parameters of a convolutional neural network are likely to be the most sensitive to depending on whether the training (and test and inference) data involves only accurately centered images versus off-centered images.</p>
  
  <p>More convolutional layers, wider convolution kernels, more dense layers, wider dense layers, more or less pooling, or ???</p>
  
  <p>e.g. If I can preprocess the data to include only accurately centered images, which hyper-parameters should I experiment with changing to create a smaller CNN model (for a power and memory constrained inference engine)?  Or conversely, if I have a minimized model trained on centered data, which hyper-parameters would I most likely need to increase to get similar loss and accuracy on uncentered (shifted in XY) data?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-01T14:12:53.633Z
Comments.Text: Hi and welcome to this community! Maybe you should provide a rigorous definition of "centered images" and "off-centered images" for your question to be answered unambiguously.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-01-02T08:16:18.067Z
Comments.Text: Translation augmentation.
Comments.UserDisplayName: mirror2image
Tags: <convolutional-neural-networks><training><hyper-parameters>
Title: Which CNN hyper-parameters are most sensitive to centered versus off centered data?
UserDisplayName: hotpaw2
ThreadText: # Which CNN hyper-parameters are most sensitive to centered versus off centered data?
  
  (Asked by: username_0 on 2019-12-31)
  
  Which hyper-parameters of a convolutional neural network are likely to be the most sensitive to depending on whether the training (and test and inference) data involves only accurately centered images versus off-centered images.
  
  More convolutional layers, wider convolution kernels, more dense layers, wider dense layers, more or less pooling, or ???
  
  e.g. If I can preprocess the data to include only accurately centered images, which hyper-parameters should I experiment with changing to create a smaller CNN model (for a power and memory constrained inference engine)? Or conversely, if I have a minimized model trained on centered data, which hyper-parameters would I most likely need to increase to get similar loss and accuracy on uncentered (shifted in XY) data?
  
  **Tags:** convolutional-neural-networks, training, hyper-parameters
  
  > **Comments:**
  > **username_1**: Hi and welcome to this community! Maybe you should provide a rigorous definition of "centered images" and "off-centered images" for your question to be answered unambiguously.  
  > **username_2**: Translation augmentation.  
  
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  ## 0 answers
  
  