How to obtain figure 2(d) about cct HOT 5 CLOSED

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.

from cct.

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?

from cct.

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

from cct.