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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?

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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
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