Methods › General › Normalization › Virtual Batch Normalization
Virtual Batch Normalization
Introduced by Tim Salimans et al. in Improved Techniques for Training GANs
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Virtual Batch Normalization is a normalization method used for training generative adversarial networks that extends batch normalization. Regular batch normalization causes the output of a neural network for an input example 𝐱 to be highly dependent on several other inputs 𝐱′ in the same minibatch. To avoid this problem in virtual batch normalization (VBN), each example 𝐱 is normalized based on the statistics collected on a reference batch of examples that are chosen once and fixed at the start of training, and on 𝐱 itself. The reference batch is normalized using only its own statistics. VBN is computationally expensive because it requires running forward propagation on two minibatches of data, so the authors use it only in the generator network.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Improved Techniques for Training GANs 10 Jun 2016 · 46 repositories · arXiv:1606.03498Syntology ran 1 of 2 samples · 1 unverified
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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