This can be used to implement seamless voice performance transfer from one speaker to another: 1. Train a WaveNet with the source speaker. 2. Train a second WaveNet with the target speaker. Or for something totally new, train a WaveNet with a bunch of different speakers until you get one you like. This becomes the target WaveNet . 3. Record raw audio from the source speaker. Fun fact: any algorithmic process that "re…
> Fun fact: any algorithmic process that "renders" something given a set of inputs can be "run in reverse" Now wait a minute, most algorithms cannot be run in reverse! The only general way to reverse an algo is to try all possible inputs, which has exponential complexity. That's the basis of RSA encryption. Maybe you're thinking about automatic differentiation, a general algo to get the gradient of the output w.r.t.…
E.g. a forward problem might be to solve some PDE to simulate the state of a system from some known initial conditions.
The inverse problem could be to try to reverse engineer what the initial conditions were given the observed state of the system.
Inverse problems are typically much harder to deal with, and much harder to solve. E.g. perhaps they don't have a unique solution, or the solution is a highly discontinuous function of the inputs, which amplifies any measurement errors. In practice this can be addressed by regularisation aka introducing strong structural assumptions about what the expected solution should be like. This can be quite reasonable from a Bayesian perspective.
https://en.wikipedia.org/wiki/Inverse_problem#Mathematical_c...