We propose a meta-learning method for positive and unlabeled (PU)
classification, which improves the performance of binary classifiers obtained
from only PU data in unseen target tasks. PU learning is an important problem
since PU data naturally arise in real-world applications such as outlier
detection and information retrieval. Existing PU learning methods require many
PU data, but sufficient data are often unavailable in practice. The proposed
method minimizes the test classification risk after the model is adapted to PU
data by using related tasks that consist of positive, negative, and unlabeled
data. We formulate the adaptation as an estimation problem of the Bayes optimal
classifier, which is an optimal classifier to minimize the classification risk.
The proposed method embeds each instance into a task-specific space using
neural networks. With the embedded PU data, the Bayes optimal classifier is
estimated through density-ratio estimation of PU densities, whose solution is
obtained as a closed-form solution. The closed-form solution enables us to
efficiently and effectively minimize the test classification risk. We
empirically show that the proposed method outperforms existing methods with one
synthetic and three real-world datasets.

我们提出了一种元学习方法，用于正负样本未标记分类，从而提高仅使用 PU 数据获得的二元分类器在未知目标任务中的性能。该方法使用包含正向、负向和未标记数据的相关任务，在使用 PU 数据调整模型之后，最小化了测试分类风险。通过使用神经网络将每个实例嵌入到任务特定空间中，我们以密度比率估计 PU 密度的形式估计了贝叶斯最优分类器，其解可作为闭式解求得。经验证明，该方法在一个合成数据集和三个真实数据集上表现优于现有方法。

正标 - 未标分类的元学习

Meta-learning for Positive-unlabeled Classification

This paper defines a positive and unlabeled classification problem for
standard GANs, which then leads to a novel technique to stabilize the training
of the discriminator in GANs. Traditionally, real data are taken as positive
while generated data are negative. This positive-negative classification
criterion was kept fixed all through the learning process of the discriminator
without considering the gradually improved quality of generated data, even if
they could be more realistic than real data at times. In contrast, it is more
reasonable to treat the generated data as unlabeled, which could be positive or
negative according to their quality. The discriminator is thus a classifier for
this positive and unlabeled classification problem, and we derive a new
Positive-Unlabeled GAN (PUGAN). We theoretically discuss the global optimality
the proposed model will achieve and the equivalent optimization goal.
Empirically, we find that PUGAN can achieve comparable or even better
performance than those sophisticated discriminator stabilization methods.

本文定义了一个基于正例和未标记的分类问题，进而提出了一种新颖的技术来稳定 GAN 中的鉴别器训练。我们将生成数据视为未标记，这个分类器称为 Positive-Unlabeled GAN（PUGAN），理论上证明了这个模型将达到全局最优性。实验结果表明，PUGAN 可以达到与一些复杂鉴别器稳定化方法相当甚至更好的性能。