Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging
domain adaptation setting where the feature space differs between source and
target domains, and the target domain has only unlabeled data. Existing HUDA
methods assume that both positive and negative examples are available in the
source domain, which may not be satisfied in some real applications. This paper
addresses a new challenging setting called positive and unlabeled heterogeneous
domain adaptation (PU-HDA), a HUDA setting where the source domain only has
positives. PU-HDA can also be viewed as an extension of PU learning where the
positive and unlabeled examples are sampled from different domains. A naive
combination of existing HUDA and PU learning methods is ineffective in PU-HDA
due to the gap in label distribution between the source and target domains. To
overcome this issue, we propose a novel method, positive-adversarial domain
adaptation (PADA), which can predict likely positive examples from the
unlabeled target data and simultaneously align the feature spaces to reduce the
distribution divergence between the whole source data and the likely positive
target data. PADA achieves this by a unified adversarial training framework for
learning a classifier to predict positive examples and a feature transformer to
transform the target feature space to that of the source. Specifically, they
are both trained to fool a common discriminator that determines whether the
likely positive examples are from the target or source domain. We
experimentally show that PADA outperforms several baseline methods, such as the
naive combination of HUDA and PU learning.

该论文提出了一个新的异构无监督领域自适应 (UDA) 任务：正例与未标记数据异构领域自适应 (positive and unlabeled heterogeneous domain adaptation, PU-HDA)，并针对该任务提出了一种名为 PADA 的新颖方法，该方法通过对抗训练框架学习分类器，以预测可能的正例，并学习特征转换器将目标领域的特征空间对齐到源领域的特征空间，从而减小源域数据和目标域的可能正例数据之间的分布差异。

正样本和未标注数据的异构领域自适应

Heterogeneous Domain Adaptation with Positive and Unlabeled Data

Multi-label learning deals with the classification problems where each
instance can be assigned with multiple labels simultaneously. Conventional
multi-label learning approaches mainly focus on exploiting label correlations.
It is usually assumed, explicitly or implicitly, that the label sets for
training instances are fully labeled without any missing labels. However, in
many real-world multi-label datasets, the label assignments for training
instances can be incomplete. Some ground-truth labels can be missed by the
labeler from the label set. This problem is especially typical when the number
instances is very large, and the labeling cost is very high, which makes it
almost impossible to get a fully labeled training set. In this paper, we study
the problem of large-scale multi-label learning with incomplete label
assignments. We propose an approach, called MPU, based upon positive and
unlabeled stochastic gradient descent and stacked models. Unlike prior works,
our method can effectively and efficiently consider missing labels and label
correlations simultaneously, and is very scalable, that has linear time
complexities over the size of the data. Extensive experiments on two real-world
multi-label datasets show that our MPU model consistently outperform other
commonly-used baselines.

本文研究了多标签学习的不完整标签分配问题，提出了一种名为 MPU 的方法，该方法基于正数和未标记随机梯度下降和堆叠模型，可以同时有效且高效地考虑丢失的标签和标签之间的关联性，从而更好地解决了大规模多标签学习问题。