Data distortion is commonly applied in vision models during both training (e.g methods like MixUp and CutMix) and evaluation (e.g. shape-texture bias and robustness). This data modification can introduce artificial information. It is often assumed that the resulting artefacts are detrimental to training, whilst being negligible when analysing models. We investigate these assumptions and conclude that in some cases they are unfounded and lead to incorrect results. Specifically, we show current shape bias identification methods and occlusion robustness measures are biased and propose a fairer alternative for the latter. Subsequently, through a series of experiments we seek to correct and strengthen the community's perception of how augmenting affects learning of vision models. Based on our empirical results we argue that the impact of the artefacts must be understood and exploited rather than eliminated.

翻译：在培训(例如MixUp和CutMix等方法)和评价(例如形状-纺织偏差和稳健性)期间,通常在愿景模型中应用数据扭曲,这种数据修改可以引入人工信息,通常假定由此产生的人工制品对培训有害,但在分析模型时却忽略不计。我们对这些假设进行调查,得出的结论是,在某些情况下,这些假设是没有根据的,并导致不正确的结果。具体地说,我们显示了当前形状偏见识别方法和隔离稳健性措施是有偏见的,为后者提出了更公平的替代方法。随后,我们通过一系列实验,力求纠正和加强社区对于扩大对视觉模型学习的影响的认识。我们根据经验结果认为,必须理解和利用这些人工制品的影响,而不是消除这些影响。