February 2020
Intermediate to advanced
328 pages
8h 19m
English
Content preview from Deep Learning with R Cookbook
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Although GANs have become quite a popular deep learning technique, there are several challenges associated with working with GANs. The critical ones are listed here:
- GANs are extremely hard to train. Often, the model parameters destabilize and don't converge.
- Sometimes, the discriminator gets so accurate that the generator's gradient vanishes and learns nothing.
- The imbalance between the generator and the discriminator can cause overfitting.
- GANs are too sensitive to model tuning and hyperparameter selection.
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