From my understanding of unsupervised DNNs for image classification:
- The input layer is a 4,096 dimension vector (for 64 x 64 images)
- The hidden layers represent much lower "features" as identified by the back propagation
- As the model is generative, the output layer is also a 64 x 64 image
Therefore, how do can we make a prediction that a new unseen image contains a specific image class (e.g. cat) if we lack labelled data?