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Very nice. If I may ask for clarification on the CNN method (which seems to work quite well), you're taking a CNN that's built for classification but removing the last layer (which I believe is fully connected?) so that you only take the "internal features", is that correct? I would expect some kind of autoencoder would fit here, but very interesting that this works.

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This is the same idea that underlies style transfers and metrics like the FID (which is used to judge generative networks' outputs on their similarity to the test set).

The idea is that the activations within an image recognition network are similar for images that are similar and so you can measure the distance between two images in a space that has some semantic meaning.

This is the concept behind "transfer learning", taking a fully-trained CNN (ResNet, Inception, MobileNet, etc) and removing the last or last two layers. What I don't understand yet is when to use which trained network, i.e. if they have different features that make them suited for different application domains.

I don't think there's a lot to say about this, you want the pretraining on a large dataset and on a task that's similar to yours. Probably largeness is somewhat more important. These things aren't an exact science at this point.

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