Typically we think of Convolutional Neural Networks as accepting fixed size inputs (i.e., 224×224, 227×227, 299×299, etc.).
But what if you wanted to:

Utilize a pre-trained network for transfer learning…
…and then update the input shape dimensions to accept images with different dimensions than what the original network was trained on?

Why might you want to utilize different image dimensions?
There are two common reasons:

Your input image dimensions are considerably smaller than what the CNN was trained on and increasing their size introduces too many artifacts and dramatically hurts loss/accuracy.
Your images are high resolution and contain small objects that are hard to detect. Resizing to the original input dimensions of the CNN hurts accuracy and you postulate increasing resolution will help improve your model.

In these scenarios, you would wish to update the input shape dimensions of the CNN and then be able to perform transfer learning.
The question then becomes, is such an update possible?
Yes, in fact, it is.
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