: A dynamic method that identifies samples away from the standard classification plane to better represent drift variations in real-time.
: This machine learning approach treats "clean" initial data as a source domain and "drifted" data as a target domain. It uses techniques like Knowledge Distillation (KD) or Wasserstein distance to align these domains so the model remains accurate. Gas-Lab - Drift
A critical "helpful feature" or strategy for managing this issue is , which uses software-based signal processing to maintain accuracy without constant manual recalibration. Key Helpful Features & Methods : A dynamic method that identifies samples away