论文标题
为什么分类器精度显示在分配变化下的线性趋势？
Why do classifier accuracies show linear trends under distribution shift?
论文作者
论文摘要
对深度学习中的概括的最新研究观察到了一个令人困惑的趋势：一个数据分布上模型的准确性是精度在另一个分布上的线性函数。我们在模型相似性的直观假设下解释了这一趋势，该假设在先前的工作中经过经验验证。更确切地说，我们假设两个模型在其预测中同意的概率高于我们仅从其准确性水平中推断出的概率。然后，我们表明，除非分布偏移的大小较大，否则在评估两个分布的模型时必须发生线性趋势。这项工作强调了理解模型相似性的价值，这可能会影响分类模型的概括和鲁棒性。
Recent studies of generalization in deep learning have observed a puzzling trend: accuracies of models on one data distribution are approximately linear functions of the accuracies on another distribution. We explain this trend under an intuitive assumption on model similarity, which was verified empirically in prior work. More precisely, we assume the probability that two models agree in their predictions is higher than what we can infer from their accuracy levels alone. Then, we show that a linear trend must occur when evaluating models on two distributions unless the size of the distribution shift is large. This work emphasizes the value of understanding model similarity, which can have an impact on the generalization and robustness of classification models.
