Visualizing convolutional neural networks embedding

Question

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?

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).