Discriminator
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Labels to Street Scene Labels to Facade BW to Color
arxiv.orgdiscriminator, D, learns to classify between fake (synthesized by the generator) and real fedge, photogtuples. The generator, G, learns to fool the discriminator. Unlike an unconditional GAN, both the generator and discriminator observe the input edge map. large …
Conditional Image Synthesis with Auxiliary Classifier GANs
arxiv.orgThe discriminator D receives as input either a training image or a synthesized image from the generator and outputs a probability distri-bution P(SjX) = D(X) over possible image sources. The discriminator is trained to maximize the log-likelihood it assigns to the correct source: L= E[logP(S= real jX real)]+ E[logP(S= fakejX fake)] (1)
![arXiv:2108.02774v1 [cs.CV] 5 Aug 2021](/cache/preview/3/a/d/f/b/9/3/2/thumb-3adfb9327add494116d19fc92cd00759.jpg)
arXiv:2108.02774v1 [cs.CV] 5 Aug 2021
arxiv.orgerator and discriminator pair using transfer learning [13, 71]. The fine-tuning can improve upon training from scratch [67], but it can also easily overfit on the new training data. To avoid overfitting, several groups propose to limit the changes in model weights: Batch Statistic Adaptation preserves all