: 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.
In the context of gas sensing and electronic noses, refers to the gradual, unpredictable shift in sensor responses over time, often caused by sensor aging, contamination, or environmental changes.
