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After the training is completed, I understand the process. However, I cannot understand how the prototypes are learned at the beginning? Is it user defined? The paper doesn't say so.
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I have the same question :D it seems to be very important but how the prototypical parts were learned may be neglected. @cfchen-duke can you give us a high-level intuition? I think this is explained in the paper but not easy to understand and a high-level explanation is very useful. Thank you.
After reading the paper I understood that the prototypes are 'learned', and so the initialization is done randomly.
In the supplement Figure 10: Overview of training algorithm this is described as ∀j: prototype pj ←Uniform([0,1]H1×W1×D);
Looking at the model code I found this https://github.com/cfchen-duke/ProtoPNet/blob/master/model.py#L105 which confirms this hypothesis.
After the training is completed, I understand the process. However, I cannot understand how the prototypes are learned at the beginning? Is it user defined? The paper doesn't say so.
The text was updated successfully, but these errors were encountered: