generative deep learning random generation of the parameters in the layer_contributions dictionary to quickly explore many different layer combinations figure shows range of results obtained using different layer configurationsfrom an image of delicious homemade pastry figure trying range of deepdream configurat ions on an example image wrapping up deepdream consists of running convnet in reverse to generate inputs based on the representations learned by the network the results produced are fun and somewhat similar to the visual artifacts induced in humans by the disruption of the visual cortex via psychedelics note that the process isn' specific to image models or even to convnets it can be done for speechmusicand more licensed to