Nuisance-Label Supervision: Robustness Improvement by Free Labels

• URL: http://arxiv.org/abs/2110.07118v1
• Date: Thu, 14 Oct 2021 02:07:00 GMT
• Title: Nuisance-Label Supervision: Robustness Improvement by Free Labels
• Authors: Xinyue Wei, Weichao Qiu, Yi Zhang, Zihao Xiao, Alan Yuille
• Abstract summary: We present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Experiments show consistent improvement in robustness towards image corruption and appearance change in action recognition.
• Score: 14.711384503643995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a Nuisance-label Supervision (NLS) module, which can make models more robust to nuisance factor variations. Nuisance factors are those irrelevant to a task, and an ideal model should be invariant to them. For example, an activity recognition model should perform consistently regardless of the change of clothes and background. But our experiments show existing models are far from this capability. So we explicitly supervise a model with nuisance labels to make extracted features less dependent on nuisance factors. Although the values of nuisance factors are rarely annotated, we demonstrate that besides existing annotations, nuisance labels can be acquired freely from data augmentation and synthetic data. Experiments show consistent improvement in robustness towards image corruption and appearance change in action recognition.

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