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Singular Value Clipping

3 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Singular Value Clipping (SVC) is an adversarial training technique used by TGAN to enforce the 1-Lipschitz constraint of the WGAN objective. It is a constraint to all linear layers in the discriminator that satisfies the spectral norm of weight parameter W is equal or less than one. This means that the singular values of weight matrix are all one or less. Therefore singular value decomposition (SVD) is performed after a parameter update, replacing all the singular values larger than one with one, and the parameters are reconstructed with them. The same operation is applied to convolutional layers by interpreting a higher order tensor in weight parameter as a matrix Ŵ.

Source: Temporal Generative Adversarial Nets with Singular Value ClippingSee Code · pfnet-research/tgan

Papers archive 2025-07-28

3 shown of 3, 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.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Attribute1
Image Generation1
Tabular Data Generation1
Video Generation1

Usage over time archive 2025-07-28

Papers per year tagged with Singular Value Clipping: 2016 to 2021, peak 1 1 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

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

Adversarial Training

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