通过消融重新审视CNN中单个单元的重要性
Revisiting the Importance of Individual Units in CNNs via Ablation
摘要：
我们重新审视了卷积神经网络（CNN）中各个单元对视觉识别的重要性。通过对在大规模图像数据集上训练的CNN进行单元消融实验，我们证明，尽管消融任何单个单元不会损害整体分类准确性，但它确实会对特定类别的准确性造成重大损害。这一结果表明，一个单独的单元是专门编码的信息相关的一个子集的类。我们计算单位消融下的准确性下降和一个单独的单元，如类的选择性和权重L1范数的各种属性之间的相关性。我们确认，单元属性，如类的选择性是一个穷人的预测影响的整体准确性，发现以前在最近的工作\cite{morcos2018importance}。然而，我们的研究结果表明，类的选择性以及其他属性是一个单元的重要性，个别类的良好预测。我们评估了随机旋转、批量归一化和丢弃对单元对特定类的重要性的影响。我们的研究结果表明，具有高选择性的单位在网络分类能力中起着重要的作用，在个人类的水平。理解和解释这些单元的行为是必要的和有意义的。
英文摘要：
We revisit the importance of the individual units in Convolutional Neural Networks (CNNs) for visual recognition. By conducting unit ablation experiments on CNNs trained on large scale image datasets, we demonstrate that, though ablating any individual unit does not hurt overall classification accuracy, it does lead to significant damage on the accuracy of specific classes. This result shows that an individual unit is specialized to encode information relevant to a subset of classes. We compute the correlation between the accuracy drop under unit ablation and various attributes of an individual unit such as class selectivity and weight L1 norm. We confirm that unit attributes such as class selectivity are a poor predictor for impact on overall accuracy as found previously in recent work \cite{morcos2018importance}. However, our results show that class selectivity along with other attributes are good predictors of the importance of one unit to individual classes. We evaluate the impact of random rotation, batch normalization, and dropout to the importance of units to specific classes. Our results show that units with high selectivity play an important role in network classification power at the individual class level. Understanding and interpreting the behavior of these units is necessary and meaningful.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
