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"In terms of probabilities, the classifier wants to find $p(y|x)$, or the likelihood that given an image input $x$, you want to know the probability of its class $y$ being cat, $p(y=\\text{cat}|x)$, or dog, $p(y=\\text{dog}|x)$.\n",
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"In terms of probabilities, the classifier wants to find $p(y|x)$, or the likelihood that given an image input $x$, you want to know the probability of its class $y$ being cat, $p(y=\\text{cat}|x)$, or dog, $p(y=\\text{dog}|x)$.\n",
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"The discriminator is simply a classifier with two classes: real and fake. Given an input x, the discriminator will calculate the probabilities $p(real|x)$ and $p(fake|x)$ and classify $x$. The input $x$ can be anything that you have the generator create and is not limited to images. Your GAN can be trained on videos, text, audio, etc.\n"
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"The discriminator is simply a classifier with two classes: real and fake. Given an input x, the discriminator will calculate the probabilities $p(y=\\text{real}|x)$ and $p(y=\\text{fake}|x)$ and classify $x$. The input $x$ can be anything that you have the generator create and is not limited to images. Your GAN can be trained on videos, text, audio, etc.\n"
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