In this article, the author creates a graph (at the end of the post) from the embeddings of different words found by transformer model. I would like to do a similar thing for a convolutional neural network in order to be able to evaluate clusters. The final objective is to be able to identify similar images in the train set to a given image.

I thought about extracting the hidden representation created by one of the hidden layers and reduce the dimensions to 2 using something like PCA.

I have some doubts:

  • Is this strategy sound?
  • Which layer should I use? Should I use the last one, as when creating a Global Class Activation map?

scatter plot


1 Answer 1


Yes, the approach you propose is sound and applied widely.

Instead of PCA, I suggest using U-MAP, which will probably yield better results (also better than t-SNE).

The representation you may use as input to U-MAP is the output last layer before the projection to the label space dimensionality (e.g. with a 5-class classifier, you would take the vector representation before projection to the 5-dimensional space).

  • $\begingroup$ Thank you, that is very helpful. I should take the output layer before without multiplying by the classifier weights right? $\endgroup$
    – Zan
    Feb 8 at 23:20
  • $\begingroup$ Yes, that's it. $\endgroup$
    – noe
    Feb 9 at 7:21
  • 1
    $\begingroup$ Accepted, thank you for your help! $\endgroup$
    – Zan
    Feb 9 at 12:04

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