Mini-batch discrimination

Mini-batch discrimination is another approach to stabilize the training of GANs. It was proposed by Ian Goodfellow and others in Improved Techniques for Training GANs, which is available at https://arxiv.org/pdf/1606.03498.pdf. To understand this approach, let's first look in detail at the problem. While training GANs, when we pass the independent inputs to the discriminator network, the coordination between the gradients might be missing, and this prevents the discriminator network from learning how to differentiate between various images generated by the generator network. This is mode collapse, a problem we looked at earlier. To tackle this problem, we can use mini-batch discrimination. The following diagram illustrates ...

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