Image Data Augmentation
Greedy Policy Search
Introduced by Molchanov et al. in Greedy Policy Search: A Simple Baseline for Learnable Test-Time AugmentationGreedy Policy Search (GPS) is a simple algorithm that learns a policy for test-time data augmentation based on the predictive performance on a validation set. GPS starts with an empty policy and builds it in an iterative fashion. Each step selects a sub-policy that provides the largest improvement in calibrated log-likelihood of ensemble predictions and adds it to the current policy.
Source: Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Autonomous Driving | 10 | 10.75% |
| Time Series | 8 | 8.60% |
| Semantic Segmentation | 4 | 4.30% |
| Image Classification | 3 | 3.23% |
| Indoor Localization | 3 | 3.23% |
| Visual Navigation | 3 | 3.23% |
| Anomaly Detection | 3 | 3.23% |
| Federated Learning | 3 | 3.23% |
| Edge-computing | 3 | 3.23% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |