Given 6000 40 X 40 photo patches taken out of 50 x-ray scans, what can be best way to extract useful features out of this patches? I need the method to:
not be too computationally costly
the latent space has to be a vector
I came across multiple kinds of autoencoders: fully connected, convolutional, fully connected variational, convolutional variational, denoising, deep belief networks and so on. I do not have the time to try them all out. So what would you recommend as most likely to work? and do you know any good implementation of it?