Regularization
Batch Nuclear-norm Maximization
Introduced by Cui et al. in Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient SituationsBatch 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.
Source: Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Papers
| Paper | Code | Results | Date | Stars |
|---|
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
