#71SQUARED DEEP DREAMER SOFTWARE#

used the total variation regularizer that prefers images that are piecewise constant. That have natural image statistics (without a preference for any particular image), or are simply smooth. The generated images can be greatly improved by including a prior or regularizer that prefers inputs This usage resembles the activity of looking for animals or other patterns in clouds.Īpplying gradient descent independently to each pixel of the input produces images in whichĪdjacent pixels have little relation and thus the image has too much high frequency information. The optimization resembles backpropagation, however instead of adjusting the network weights, the weights are held fixed and the input is adjusted.įor example, an existing image can be altered so that it is 'more cat-like', and the resulting enhanced image can be again input to the procedure. However, after enough reiterations, even imagery initially devoid of the sought features will be adjusted enough that a form of pareidolia results, by which psychedelic and surreal images are generated algorithmically. This reversal procedure is never perfectly clear and unambiguous because it utilizes a one-to-many mapping process. This can be used for visualizations to understand the emergent structure of the neural network better, and is the basis for the DeepDream concept. the one for faces or certain animals) yields a higher confidence score. However, once trained, the network can also be run in reverse, being asked to adjust the original image slightly so that a given output neuron (e.g.

#71SQUARED DEEP DREAMER SOFTWARE#

The software is designed to detect faces and other patterns in images, with the aim of automatically classifying images. The original image (top) after applying ten (middle) and fifty (bottom) iterations of DeepDream, the network having been trained to perceive dogs

71squared deep dreamer