Comments (5)
Hi @reluuu
Unfortunately, I was not able to find the notebook that contains the computation, but it should be quite easy to replicate. For the features, at each spatial location, fetch the 8 neighboring features (so 3 x 3 window) and compute the average of the L2 distances between the center the 8 neighboring features, so for each feature, you will get a scalar representing the distance between it and its neighboors; then normalize them and plot them.
Hope this helps.
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Did you use dimension reduction like PCA to produce the figure?
from cct.
No we used the original features without any reduction, but using PCA might be a good idea
from cct.
But for the hidden representation, there are 2048 channels? How to visualize them in grayscale?
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at each spatial location, you compute the L2 distances between the feature i and its neighboors j, and use these distances, which are scalars, for ploting
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