Can we normalize features extracted from pre-trained models
From experience, as long as you are aware of the fact that your results are coming from normalized values, it is okay. If normalization helps you show gradients, norms, etc. better then I am for it.
What I would be cautious about though, would be any further analysis on those feature matrices as they are normalized and not the true values. Say, if you were to study the distributions and such, you should be fine, but I am not sure what is your next step, and if this can/will be harmful.
Can you share more details around "further analysis"?
I am working with features extracted from pre-trained VGG16 and VGG19 models. The features have been extracted from second fully connected layer (FC2) of the above networks.
The resulting feature matrix (of dimensions (8000,4096)) has values in the range [0,45]. As a result, when I am using this feature matrix in gradient based optimization algorithms, the value for loss function, gradient,  norms etc. take very high values.
In order to do away with such high values, I applied MinMax normalization to this feature matrix and since then the values are manageable. Also, the optimization algorithm is behaving properly. Is my strategy OK i.e. is it fair enough to normalize features that have been extracted from a pre-trained models for further processing.