4 ms·

Thanks for the detailed answer. I don't claim it to be a novel idea, I just remember the Alpha Go (zero?) paper that said they played it against older versions

• sillysaurusx 6y ago
  Ah! This is an interesting difference, and illustrates one fun aspect of GANs vs other types of models: Alpha Go had a very specific "win condition" that you can measure precisely. (Can the model win the game?) Whereas it's very difficult to quantify what it means to be "better" at generating images, once you get to a certain threshold of realism. (Was Leonardo better than michelangelo? Probably, but it's hard to measure precisely.) The way that Alpha Go worked was, it gathered a bunch of experiences, i.e. it played a bunch of games. Then, after playing tons of games -- tens of dozens! just kidding, probably like 20 million -- it then performed a single gradient update. In other words, you gather your current experiences, and then you react to them. It's a two-phase commit. There's an explicit "gather" step, which you then react to by updating your database of parameters, so to speak. Whereas with GANs, that happens continuously. There's no "gather" step. The generator simply tries to maximize the discrimiantor's loss, and the discriminator tries to minimize it. Balancing the two has been very tricky. But the results speak for themselves.