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AlexiaJMon Connections between SVMs, Wasserstein distance and gradient-penalty GANs

Paper is here: https://arxiv.org/abs/1910.06922. Feel free to ask me questions!

AlexiaJMon The relativistic discriminator: a key element missing from standard GAN

It's from same GAN though, C(x_r) and C(x_f) come from the same neural network. It's how realistic real data is compared to fake data (and vice-versa) as determined by C (discriminator without activation function).

AlexiaJMon The relativistic discriminator: a key element missing from standard GAN

"Relative probability" is only correct for standard GAN, not for other GANs which don't estimate a probability.

My first name was "Critic's difference" since it's literally C(x_r)-C(x_f) so its the difference in critics, but it felt really unclear, it doesn't give the reader any sense of what it is about. Relativism/Relativistic is better since it's to say that it really doesn't matter if the data looks real, what matters if how realistic is real data relative to fake data (and vice versa). The frame of reference is important here.

AlexiaJMon The relativistic discriminator: a key element missing from standard GAN

Paper is here: https://arxiv.org/abs/1807.00734. Feel free to ask me questions!