Domain incremental learning (DIL) has been discussed in previous studies on
deep neural network models for classification. In DIL, we assume that samples
on new domains are observed over time. The models must classify inputs on all
domains. In practice, however, we may encounter a situation where we need to
perform DIL under the constraint that the samples on the new domain are
observed only infrequently. Therefore, in this study, we consider the extreme
case where we have only one sample from the new domain, which we call one-shot
DIL. We first empirically show that existing DIL methods do not work well in
one-shot DIL. We have analyzed the reason for this failure through various
investigations. According to our analysis, we clarify that the difficulty of
one-shot DIL is caused by the statistics in the batch normalization layers.
Therefore, we propose a technique regarding these statistics and demonstrate
the effectiveness of our technique through experiments on open datasets.

在本研究中，我们考虑了只有一个来自新领域的样本的情况，称之为一次性增量学习（one-shot DIL）。通过对批量归一化层中的统计数据进行分析，我们发现了一次性增量学习的困难来源，并提出了一种关于这些统计数据的技术。通过在开放数据集上进行实验，我们证明了我们的技术的有效性。