同时优化速度和精度的序贯密度比估计
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
摘要：
尽早和尽可能准确地对序列数据进行分类是一个具有挑战性的关键问题，特别是当采样成本很高时。实现这一目标的一种算法是序贯概率比检验（SPRT），这被称为贝叶斯最优：它可以保持数据样本的预期数量尽可能小，给定期望的误差上限。然而，原始的SPRT做了两个关键的假设，限制了它在现实世界中的应用：（i）样本是独立和同分布的，（ii）数据从每个类中导出的可能性可以精确计算。在这里，我们提出了SPRT-TANDEM，一种基于深度神经网络的SPRT算法，克服了上述两个障碍。SPRT-TANDEM通过利用用于对数似然比估计（LLLR）的新型损失函数来顺序估计两个备选假设的对数似然比，同时允许相关性高达$N (\in \mathbb{N})$之前的样本。在一个原始的和两个公共的视频数据库，NORTHMNIST，UCF101和SiW的测试中，SPRT-TANDEM实现了统计上显着更好的分类精度比其他基线分类器，数据样本数量较少。该代码和NocolumMNIST可在www.example.com上公开获取。
英文摘要：
Classifying sequential data as early and as accurately as possible is a challenging yet critical problem, especially when a sampling cost is high. One algorithm that achieves this goal is the sequential probability ratio test (SPRT), which is known as Bayes-optimal: it can keep the expected number of data samples as small as possible, given the desired error upper-bound. However, the original SPRT makes two critical assumptions that limit its application in real-world scenarios: (i) samples are independently and identically distributed, and (ii) the likelihood of the data being derived from each class can be calculated precisely. Here, we propose the SPRT-TANDEM, a deep neural network-based SPRT algorithm that overcomes the above two obstacles. The SPRT-TANDEM sequentially estimates the log-likelihood ratio of two alternative hypotheses by leveraging a novel Loss function for Log-Likelihood Ratio estimation (LLLR) while allowing correlations up to $N (\in \mathbb{N})$ preceding samples. In tests on one original and two public video databases, Nosaic MNIST, UCF101, and SiW, the SPRT-TANDEM achieves statistically significantly better classification accuracy than other baseline classifiers, with a smaller number of data samples. The code and Nosaic MNIST are publicly available at https://github.com/TaikiMiyagawa/SPRT-TANDEM.
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。
