I have a big set of images (>10.000), where there are similarities among them. I need to find a number/group of image patterns (eg, 5) that represent all images.
As I do not know what patterns are, therefore I suppose that I need an unsupervised neural network. However, as I do not know how many patterns are, I cannot specify the number of classes/labels in this network.
Reading, I think an Autoencoder network may be useful because this network is unsupervised and I don't need to specify the number of classes. However, I am not sure if this network can infer patterns (as abstractions from images) from a dataset.
BTW, I am using Tensorflow & Keras in Python.