Domain adaptation · UCI gas sensor array
Gas classification that survives sensor drift.
Sensor arrays drift for months. CORAL realigns the distribution and residualization strips dose bias, holding accuracy where baselines collapse.
Two shifts. Two fixes.
Temporal drift and concentration shift both wreck a sensor classifier, but for opposite reasons. Align the wrong statistic and accuracy gets worse, not better.
target-domain accuracy
Drifted. The target domain's second-order statistics no longer match the source, collapsing the classifier.
Train batches 1-5, test batch 10. Sensors age over months.
CORAL aligns the covariance across time, so the decision boundary transfers. Accuracy climbs from 47% to 79.9%.
Results that hold under drift.
Every number below comes from the held-out evaluation: train on early batches, test on the drifted batch the model never saw.
Temporal drift across severity
CORAL + Robust MLP dominates under severe aging. Stacking edges ahead only when drift is mild, an honest trade-off the system exposes rather than hides.
Architecture: MLP vs Transformer
Improvement trajectory
Different shifts, different fixes
Temporal drift needs covariance alignment (CORAL). Concentration shift needs dose-bias removal (residualization). One method cannot do both.
Alignment beats ensembles under severe drift
CORAL + Robust MLP reaches 79.9% where a Random-Forest/SVM stack manages 65.4%, a 14.5-point gap.
MLP inductive bias wins on tabular data
At ~13k samples the Robust MLP scores 78.3% against a sensor-tokenized Transformer's 64.3%.
Probe the model live.
Pick a gas, set the dose, then turn up sensor drift and watch the 16-channel fingerprint distort until the classifier breaks.
Target gas
Concentration
Sensor drift
Sensor array fingerprint
16-channel response signature. Each gas paints a distinct shape.
Response magnitude
How a reading survives the pipeline.
From raw conductance to a drift-proof verdict, each stage targets one failure mode the previous stage cannot fix.
Built on the UCI Gas Sensor Array Drift dataset: thousands of recordings captured as the hardware physically aged, the canonical benchmark for distribution shift in chemical sensing.
- 01
Capture
Sixteen metal-oxide sensors log conductance across 10 batches spanning 36 months of continuous operation.
- 02
Detect the drift
A t-SNE projection shows early and late batches pulling apart while the six gas classes stay internally coherent.
- 03
Align with CORAL
Recolor the target domain's covariance to match the source. No target labels required.
- 04
Residualize the dose
Regress out concentration so the model reads which gas is present, not how much of it there is.
- 05
Classify
A Robust MLP with batch-norm and dropout, tuned by Optuna, reads the realigned signature.