Methods › General › Regularization › Batch Nuclear-norm Maximization

Batch Nuclear-norm Maximization

3 papers tagged archive 2025-07-28

Introduced by Shuhao Cui et al. in Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

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

Batch Nuclear-norm Maximization is an approach for aiding classification in label insufficient situations. It involves maximizing the nuclear-norm of the batch output matrix. The nuclear-norm of a matrix is an upper bound of the Frobenius-norm of the matrix. Maximizing nuclear-norm ensures large Frobenius-norm of the batch matrix, which leads to increased discriminability. The nuclear-norm of the batch matrix is also a convex approximation of the matrix rank, which refers to the prediction diversity.

PaperSource

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
Diversity3
Domain Adaptation3
Prediction1
Semi-supervised Domain Adaptation1

Usage over time archive 2025-07-28

Papers per year tagged with Batch Nuclear-norm Maximization: 2020 to 2021, peak 2 2 0 2020: 1 paper 2020 2021: 2 papers 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

Regularization

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