<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>

CC BY-SA 4.0
2020-01-01T14:12:53.633Z
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.
nbro
CC BY-SA 4.0
2020-01-02T08:16:18.067Z
Translation augmentation.
mirror2image
<convolutional-neural-networks><training><hyper-parameters>
Which CNN hyper-parameters are most sensitive to centered versus off centered data?
hotpaw2
# 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

