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Topic: Unsupervised truth

posted 12/2/2017  22:15Send e-mail to userReply with quote
Unsupervised truth

I have had a thought of using GANs to do transmissions and self learning at the same time, with a one pixel Generating Pokemon Adversarial Network system, GANs.

There is problem in neuroscience of what is the meaning of "Same" or "equal" is, at the level of a neuron.

GANs are make up of two NNs. The detector and the re generator NN, that regenerate what is the detected NN has seen.

The detector NN detects some random color and then turn to the
output of generator NN.
A standard value is inputted into the generator NN from the detector NN that
indicates training or detector activation. When the detector NN
sees its detection, on the output, of generator NN it fire of it activation that
lock the weight of the generator NN. Complete dynamic random noise is used for the
weights values.

If you had a alternating row of detector NN (DeNN) and re generator NN (ReNN). The information could be past along in a daisy chain fashion, for a global reference.
Like so:
DeNN, ReNN, DeNN, ReNN, DeNN, ReNN, DeNN, ReNN, DeNN, ReNN.............................

The weight are randomly selected and locked in place, within the Detector NN.
No training.
So that a the color and brightness it detects is a random catch.
With many one pixel GANs in the first layer of NN system there will be over lap.
The output of these tiny GANs will combined on the next
layers, of the Deep NN, to from edge detection and blob detection.
These NNs here only detect and regenerate the color of one pixel or a averaged
of small group of pixels. So it will not be too slow to auto train.

This way different GANs captures can be compared against each other.

Generative Adversarial Nets - Fresh Machine Learning #2:

 Generative Adversarial Nets - Fresh Machine Learning #2:
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