improve its samples at all another tip that can really help a lot is a technique that I call one-sided label smoothing and we also introduced this in the paper with Tim that we're showing tonight the basic idea of one-sided label smoothing is that usually when you train the discriminator you're turning it to output hard ones on the data and hard zeros on the fake samples but it's much better if you turn it to output a soft value like 0.9 on the data and on the fixed samples it should still strive to output zeros that's why it's