How do we predict what is in an image using unsupervised deep neural networks?
After a lot of reading, I think I now understand. We really need to build 2 models.

Model 1


Unsupervised
Lots of unlabelled images
Used to 'learn features' (i.e. better that we have done manually through years of research e.g. edge detection, colour features etc).


Model 2


Supervised
Few labelled images
Use model 1 as the 'feature extractor'. i.e. pass a training image through model 1 and use the output layer as the feature vector.
Use the same approach to test images e.g. model 1 to extract features, then use the second model to output label predictions