Earlier, label/target values for a classifier were 0 or 1; 0 for fake images and 1 for real images. Because of this, GANs were prone to adversarial examples, which are inputs to a neural network that result in an incorrect output from the network. Label smoothing is an approach to provide smoothed labels to the discriminator network. This means we can have decimal values such as 0.9 (true), 0.8 (true), 0.1 (fake), or 0.2 (fake), instead of labeling every example as either 1 (true) or 0 (fake). We smooth the target values (label values) of the real images as well as of the fake images. Label smoothing can reduce the risk of adversarial examples in GANs. To apply label smoothing, assign the labels 0.9, 0.8, and 0.7, and 0.1, 0.2, and 0.3, to the images. To find out more about label smoothing, refer to the following paper: https://arxiv.org/pdf/1606.03498.pdf.