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Adversarial networks can be viewed as different agents competing against each other. But beyond that, I don't see how game theory is relevant or what insights i

Adversarial networks can be viewed as different agents competing against each other. But beyond that, I don't see how game theory is relevant or what insights it provides. This article is super vague and confusing to me. >Where do these regularizations come from and how can we select a good regularization? How do we handle impartial information? This is where a game theoretic viewpoint becomes important. Generalization is sometimes referred to as ‘structural risk minimization’. Ok... and what useful insight do the game theorists have here? He doesn't elaborate at all. Regularization is a very well studied concept already. I don't see how this is connected to game theory at all. Regularization is all about priors and Occam's razor.

westoncb 10y ago
If nothing else, I think the idea was initially important because it provided the insight to pit two networks against each other in a competition as an optimization strategy. Now, since the competing networks can be formally characterized as a minimax two-player game, other theorems relating to these games may provide further insight in how to proceed.
kem 10y ago
The article is weird but also interesting to me at the same time. My background is in more traditional statistical modeling. The adversarial networks paradigm is really fascinating to me because it's a new way to think about model fit problems. It may turn out to be the same as some traditional model fit statistics approaches, but they're usually not thought of as competitive, which seems to open up a new way of thinking of it. At some point in the author's discussion of game theory, I began thinking that if DL does go that route, it will eventually rediscover Bayesian statistics, which can be derived from decision-theoretic principles. It seems like many things in DL have parallels in traditional statistical theory, but are expressed in a mechanistic way. I guess my broader point is that I had the opposite reaction as a lot of people: rather than wondering how game theory might be relevant, to me it seemed inevitable, given the important role it plays in basic derivations of major inferential paradigms. Those derivations usually leave the mechanisms of decision theory a black box, and in DL the making the contents of the box is the focus.
attractivechaos 10y ago
> I don't see how game theory is relevant or what insights it provides. The wiki entry of Nash equilibrium [1] and this recent preprint on improving GAN training [2] may help you understand the link between GAN and game theory. To me, this link seems plausible but very vague so far. Training GAN has not directly benefited from the math foundation of game theory. Correct me if I am wrong. [1]: https://en.wikipedia.org/wiki/Nash_equilibrium https://en.wikipedia.org/wiki/Nash_equilibrium [2]: https://arxiv.org/abs/1606.03498 https://arxiv.org/abs/1606.03498
0binbrain 10y ago
I believe that the key point here is that as adversarial networks compete against each other repeatedly they eventually will converge on a Nash Equilibrium. Nash Equilibrium from Wikipedia: "Amy is making the best decision she can, taking into account Phil's decision while Phil's decision remains unchanged, and Phil is making the best decision he can, taking into account Amy's decision while Amy's decision remains unchanged." At the point this happens, a stable solution exists which is almost like saying that we have made the most rational decision given the information we had. Nash was novel in this theory to say that if the participants were rational they would converge on an equilibrium and not get lost in endless recursion. Endless recursion being something like participants endlessly guessing the other opponent move... If Amy knows Phil knows Amy knows Phil knows Amy knows Phil... Obviously it would be problematic for DL to plunge into infinite recursion. I like to think of the Nash Equilibrium as a balanced teeter totter. When the teeter totter becomes off balanced rational systems will relearn to balance the odds again based the properties of risk (what Nash referred to as utility I believe). Once they do so, they once again converge on an equilibrium.
- PeterisP 10y ago
  The problem with statement "as adversarial networks compete against each other repeatedly they eventually will converge on a Nash Equilibrium" is that they often won't, and a big problem with training GANs is to ensure that they converge. Intuitively, what can happen is that one of the problems is simpler to learn than another, and when one of the "players" becomes overwhelmingly good, then the other part of the network stops receiving useful feedback and is unable to find out a direction for improvement, the learning stops. A "sufficiently smart" method would be able to go to a Nash equilibrium even in this case, but current GAN methods can not, so you need to take steps to ensure that it doesn't happen - e.g. extra normalization or less training for the "simpler to learn" component. It's not enough to train the "adversarial" networks to compete during each inference, on a meta-level you must ensure a form of cooperation to ensure that they are effective teachers for each other during the learning process. For a real life analogy, there's a difference between effective behavior during combat and effective behavior during sparring.
  - 10y ago

westoncb 10y ago

If nothing else, I think the idea was initially important because it provided the insight to pit two networks against each other in a competition as an optimization strategy. Now, since the competing networks can be formally characterized as a minimax two-player game, other theorems relating to these games may provide further insight in how to proceed.

kem 10y ago

The article is weird but also interesting to me at the same time. My background is in more traditional statistical modeling. The adversarial networks paradigm is really fascinating to me because it's a new way to think about model fit problems. It may turn out to be the same as some traditional model fit statistics approaches, but they're usually not thought of as competitive, which seems to open up a new way of thinking of it. At some point in the author's discussion of game theory, I began thinking that if DL does go that route, it will eventually rediscover Bayesian statistics, which can be derived from decision-theoretic principles. It seems like many things in DL have parallels in traditional statistical theory, but are expressed in a mechanistic way. I guess my broader point is that I had the opposite reaction as a lot of people: rather than wondering how game theory might be relevant, to me it seemed inevitable, given the important role it plays in basic derivations of major inferential paradigms. Those derivations usually leave the mechanisms of decision theory a black box, and in DL the making the contents of the box is the focus.

attractivechaos 10y ago

> I don't see how game theory is relevant or what insights it provides. The wiki entry of Nash equilibrium [1] and this recent preprint on improving GAN training [2] may help you understand the link between GAN and game theory. To me, this link seems plausible but very vague so far. Training GAN has not directly benefited from the math foundation of game theory. Correct me if I am wrong. [1]: https://en.wikipedia.org/wiki/Nash_equilibrium https://en.wikipedia.org/wiki/Nash_equilibrium [2]: https://arxiv.org/abs/1606.03498 https://arxiv.org/abs/1606.03498

0binbrain 10y ago

I believe that the key point here is that as adversarial networks compete against each other repeatedly they eventually will converge on a Nash Equilibrium. Nash Equilibrium from Wikipedia: "Amy is making the best decision she can, taking into account Phil's decision while Phil's decision remains unchanged, and Phil is making the best decision he can, taking into account Amy's decision while Amy's decision remains unchanged." At the point this happens, a stable solution exists which is almost like saying that we have made the most rational decision given the information we had. Nash was novel in this theory to say that if the participants were rational they would converge on an equilibrium and not get lost in endless recursion. Endless recursion being something like participants endlessly guessing the other opponent move... If Amy knows Phil knows Amy knows Phil knows Amy knows Phil... Obviously it would be problematic for DL to plunge into infinite recursion. I like to think of the Nash Equilibrium as a balanced teeter totter. When the teeter totter becomes off balanced rational systems will relearn to balance the odds again based the properties of risk (what Nash referred to as utility I believe). Once they do so, they once again converge on an equilibrium.

PeterisP 10y ago
The problem with statement "as adversarial networks compete against each other repeatedly they eventually will converge on a Nash Equilibrium" is that they often won't, and a big problem with training GANs is to ensure that they converge. Intuitively, what can happen is that one of the problems is simpler to learn than another, and when one of the "players" becomes overwhelmingly good, then the other part of the network stops receiving useful feedback and is unable to find out a direction for improvement, the learning stops. A "sufficiently smart" method would be able to go to a Nash equilibrium even in this case, but current GAN methods can not, so you need to take steps to ensure that it doesn't happen - e.g. extra normalization or less training for the "simpler to learn" component. It's not enough to train the "adversarial" networks to compete during each inference, on a meta-level you must ensure a form of cooperation to ensure that they are effective teachers for each other during the learning process. For a real life analogy, there's a difference between effective behavior during combat and effective behavior during sparring.
- 10y ago