GPS
Greedy Policy Search
**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.
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
https://paperswithcode.com/paper/greedy-policy-search-a-simple-baseline-for
Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation