A major challenge in studying robustness in deep learning is defining the set
of ``meaningless'' perturbations to which a given Neural Network (NN) should be
invariant. Most work on robustness implicitly uses a human as the reference
model to define such perturbations. Our work offers a new view on robustness by
using another reference NN to define the set of perturbations a given NN should
be invariant to, thus generalizing the reliance on a reference ``human NN'' to
any NN. This makes measuring robustness equivalent to measuring the extent to
which two NNs share invariances, for which we propose a measure called STIR.
STIR re-purposes existing representation similarity measures to make them
suitable for measuring shared invariances. Using our measure, we are able to
gain insights into how shared invariances vary with changes in weight
initialization, architecture, loss functions, and training dataset. Our
implementation is available at: https://github.com/nvedant07/STIR.

该论文提供了一种新的研究深度学习鲁棒性的方法，使用另一个参考神经网络来定义给定神经网络应该是不变的扰动集，从而使得衡量鲁棒性等于衡量两个神经网络共享的不变。通过提出一种称为 STIR 的新的相似度测量方法，可以获得有关共享不变的见解，并考察其与权重初始化、网络结构、损失函数和训练数据集的变化之间的关系。

通过共享不变性度量神经网络的表征稳健性

Measuring Representational Robustness of Neural Networks Through Shared Invariances

We present STIR (STrongly Incremental Repair detection), a system that
detects speech repairs and edit terms on transcripts incrementally with minimal
latency. STIR uses information-theoretic measures from n-gram models as its
principal decision features in a pipeline of classifiers detecting the
different stages of repairs. Results on the Switchboard disfluency tagged
corpus show utterance-final accuracy on a par with state-of-the-art incremental
repair detection methods, but with better incremental accuracy, faster
time-to-detection and less computational overhead. We evaluate its performance
using incremental metrics and propose new repair processing evaluation
standards.

本文介绍了 STIR（STrongly Incremental Repair detection），一种能够利用 n-gram 模型进行增量检测语音修复的系统，它在 Switchboard disfluency 打上标签的语料库上发挥出了与现有最先进的增量修复检测方法同等水平的准确性，并且具有更好的增量准确性、更快的检测时间和更少的计算开销。本文还针对增量修复方法提出了新的评估标准。