Greedy 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 AugmentationPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Autonomous Driving | 19 | 6.91% |
Time Series | 15 | 5.45% |
Management | 10 | 3.64% |
Semantic Segmentation | 10 | 3.64% |
BIG-bench Machine Learning | 10 | 3.64% |
Object Detection | 8 | 2.91% |
Anomaly Detection | 7 | 2.55% |
Indoor Localization | 6 | 2.18% |
Decision Making | 6 | 2.18% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |