generalized adversarial loss
Generalized adversarial loss refers to a loss function used in the field of machine learning, particularly in generative models, which encourages the generator to create realistic outputs that can fool a discriminator. It measures the ability of the generator to produce outputs that are indistinguishable from real examples, promoting the overall improvement of the generator's performance.
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