论文标题
顺序密度比估计速度和准确性的同时优化
Sequential Density Ratio Estimation for Simultaneous Optimization of Speed and Accuracy
论文作者
论文摘要
尽早和准确地对顺序数据进行分类是一个具有挑战性但又关键的问题，尤其是当抽样成本很高时。实现此目标的一种算法是顺序概率比测试（SPRT），该算法被称为贝叶斯（SPRT）：鉴于所需的误差上限，它可以使预期的数据样本的预期数量保持尽可能小。但是，原始SPRT提出了两个关键的假设，这些假设限制了其在实际情况下的应用：（i）样本是独立且相同分布的，并且（ii）可以精确计算从每个类得出的数据的可能性。在这里，我们提出了SPRT-Tandem，这是一种基于神经网络的深神网络算法，它克服了以上两个障碍。 SPRT坦德姆通过利用新的损失函数来估算两个替代假设的对数可能性比率，以实现对数 - 样比率估计（LLLR）的新损失函数，同时允许相关性高达$ n（\ in \ in \ mathbb {n}）$ tamples。在对一个原始和两个公共视频数据库的测试中，Nosaic MNIST，UCF101和SIW的测试，SPRT倾斜在统计学上的分类精度比其他基线分类器的分类精度明显好得多，并且具有较少的数据示例。代码和鼻型MNIST可在https://github.com/taikimiyagawa/sprt-tandem上公开获得。
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.
