Instead of fixing the labels, can we learn the optimal label distribution for the model to generalize better?
You will often see HLDiffForAdaptivity alongside it. This represents the "High-to-Low" difference, creating a "hysteresis" effect so the radio doesn't rapidly toggle between busy and idle states. l2hforadaptivity
The system learns which low-level features are relevant for high-level tasks. Irrelevant variations (e.g., lighting changes in a robot’s camera) are filtered out, while critical changes (e.g., a sudden drop in floor traction) are propagated upward. Instead of fixing the labels, can we learn
If the L2HForAdaptivity threshold is set too low (making the device "too sensitive"), your Wi-Fi might experience high latency or frequent pauses because it thinks the environment is busier than it actually is. The system learns which low-level features are relevant
How does this actually look in code? Usually, L2H involves a bi-level optimization loop.