Regularization
Probability Guided Maxout
A regularization criterion that, differently from dropout and its variants, is deterministic rather than random. It grounds on the empirical evidence that feature descriptors with larger L2-norm and highly-active nodes are strongly correlated to confident class predictions. Thus, the criterion guides towards dropping a percentage of the most active nodes of the descriptors, proportionally to the estimated class probability
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decision Making | 4 | 8.89% |
| Visual Reasoning | 3 | 6.67% |
| Semantic Segmentation | 3 | 6.67% |
| Time Series Analysis | 2 | 4.44% |
| Benchmarking | 1 | 2.22% |
| Language Modelling | 1 | 2.22% |
| Large Language Model | 1 | 2.22% |
| Fairness | 1 | 2.22% |
| Synthetic Data Generation | 1 | 2.22% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
