Example: barber
Introduction to Bayesian Learning
Learning techniques promise the best of both worlds: starting from some captured data, we can proce-durally synthesize more data in the style of the original. Moreover, we can constrain the synthetic data, for example, according to the requirements of an artist. Of course, we must begin with some data produced by an artist or a capture session.
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