4 ms·

So in anycase the model had too many degrees of freedom, was not parsimoinous and therefore had a low information criterion. If your model can encode the entire

So in anycase the model had too many degrees of freedom, was not parsimoinous and therefore had a low information criterion. If your model can encode the entire information set, then you arent prediciting you are simply recalling. What a silly article.

candiodari 8y ago
Not at all. A CycleGAN is a trick where the neural network gets to ask another network questions, and both networks get judged on the answers. The first network found a trick to very subtly embed the perfect answer in the question, and therefore, scored very well without solving the actual problem.