DNN会选择哪些提示？参数空间视角下的研究
Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective
摘要：
深度神经网络（DNN）通常依赖于易于学习的区分特征或线索，这些特征或线索对手头的问题不一定是必不可少的。例如，图像中的鸭子可以基于它们的典型背景风景（诸如湖泊或溪流）来识别。这种现象，也被称为捷径学习，正在成为当前一代机器学习模型的关键限制。在这项工作中，我们介绍了一组实验，以加深我们对捷径学习及其影响的理解。我们设计了一个训练设置，有几个快捷提示，命名为WCST-ML，其中每个提示都同样有利于手头的视觉识别问题。即使在机会均等的情况下，我们也观察到：（1）某些线索比其他线索更受欢迎;（2）偏向于易学线索的解决方案往往收敛到损失曲面上相对平坦的最小值;（3）专注于这些首选线索的解决方案在参数空间中要丰富得多。我们解释了丰富的某些线索通过他们的柯尔莫哥洛夫复杂性：解决方案对应的柯尔莫哥洛夫简单的线索是丰富的参数空间，因此首选DNN。我们的研究是基于合成数据集DSprites和人脸数据集UTKFace。在我们的WCST-ML中，我们观察到模型的先天偏见倾向于简单的线索，如颜色和种族。我们的研究结果强调了积极的人类干预的重要性，以消除可能导致负面社会影响的先天模型偏见。
英文摘要：
Deep neural networks (DNNs) often rely on easy-to-learn discriminatory features, or cues, that are not necessarily essential to the problem at hand. For example, ducks in an image may be recognized based on their typical background scenery, such as lakes or streams. This phenomenon, also known as shortcut learning, is emerging as a key limitation of the current generation of machine learning models. In this work, we introduce a set of experiments to deepen our understanding of shortcut learning and its implications. We design a training setup with several shortcut cues, named WCST-ML, where each cue is equally conducive to the visual recognition problem at hand. Even under equal opportunities, we observe that (1) certain cues are preferred to others, (2) solutions biased to the easy-to-learn cues tend to converge to relatively flat minima on the loss surface, and (3) the solutions focusing on those preferred cues are far more abundant in the parameter space. We explain the abundance of certain cues via their Kolmogorov (descriptional) complexity: solutions corresponding to Kolmogorov-simple cues are abundant in the parameter space and are thus preferred by DNNs. Our studies are based on the synthetic dataset DSprites and the face dataset UTKFace. In our WCST-ML, we observe that the inborn bias of models leans toward simple cues, such as color and ethnicity. Our findings emphasize the importance of active human intervention to remove the inborn model biases that may cause negative societal impacts.
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