论文标题
关于数据集质量和异质性在模型信心中的作用
On the Role of Dataset Quality and Heterogeneity in Model Confidence
论文作者
论文摘要
安全至关重要的应用需要机器学习模型，以输出准确和校准的概率。虽然已知未校准的深层网络会产生过度自信的预测，但尚不清楚模型置信度如何受到数据变化（例如标签噪声或班级大小）的影响。在本文中，我们通过研究数据集大小和标签噪声对模型置信度的影响来研究数据集质量的作用。从理论上讲，我们从理论上解释并在实验上证明，令人惊讶的是，训练数据中的标签噪声会导致不受信心的网络，而缩小的数据集大小会导致过度支持模型。然后，我们研究数据集异质性的影响，其中数据质量在各个类别对模型置信度方面有所不同。我们证明，这导致了测试数据中的异质置信度/准确性行为，并且通过标准校准算法处理不当。为了克服这一点，我们提出了一种直观的异源校准技术，并表明所提出的方法可改善CIFAR数据集上的校准指标（平均值和最差案例误差）。
Safety-critical applications require machine learning models that output accurate and calibrated probabilities. While uncalibrated deep networks are known to make over-confident predictions, it is unclear how model confidence is impacted by the variations in the data, such as label noise or class size. In this paper, we investigate the role of the dataset quality by studying the impact of dataset size and the label noise on the model confidence. We theoretically explain and experimentally demonstrate that, surprisingly, label noise in the training data leads to under-confident networks, while reduced dataset size leads to over-confident models. We then study the impact of dataset heterogeneity, where data quality varies across classes, on model confidence. We demonstrate that this leads to heterogenous confidence/accuracy behavior in the test data and is poorly handled by the standard calibration algorithms. To overcome this, we propose an intuitive heterogenous calibration technique and show that the proposed approach leads to improved calibration metrics (both average and worst-case errors) on the CIFAR datasets.
