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In this paper, we present a Nuisance-label Supervision (NLS) module, whichcan make models more robust to nuisance factor variations. Nuisance factors arethose irrelevant to a task, and an ideal model should be invariant to them. Forexample, an activity recognition model should perform consistently regardlessof the change of clothes and background. But our experiments show existingmodels are far from this capability. So we explicitly supervise a model withnuisance labels to make extracted features less dependent on nuisance factors.Although the values of nuisance factors are rarely annotated, we demonstratethat besides existing annotations, nuisance labels can be acquired freely fromdata augmentation and synthetic data. Experiments show consistent improvementin robustness towards image corruption and appearance change in actionrecognition.

Nuisance-Label Supervision: Robustness Improvement by Free Labels