Learning predictive models in new domains with scarce training data is a
growing challenge in modern supervised learning scenarios. This incentivizes
developing domain adaptation methods that leverage the knowledge in known
domains (source) and adapt to new domains (target) with a different probability
distribution. This becomes more challenging when the source and target domains
are in heterogeneous feature spaces, known as heterogeneous domain adaptation
(HDA). While most HDA methods utilize mathematical optimization to map source
and target data to a common space, they suffer from low transferability. Neural
representations have proven to be more transferable; however, they are mainly
designed for homogeneous environments. Drawing on the theory of domain
adaptation, we propose a novel framework, Heterogeneous Adversarial Neural
Domain Adaptation (HANDA), to effectively maximize the transferability in
heterogeneous environments. HANDA conducts feature and distribution alignment
in a unified neural network architecture and achieves domain invariance through
adversarial kernel learning. Three experiments were conducted to evaluate the
performance against the state-of-the-art HDA methods on major image and text
e-commerce benchmarks. HANDA shows statistically significant improvement in
predictive performance. The practical utility of HANDA was shown in real-world
dark web online markets. HANDA is an important step towards successful domain
adaptation in e-commerce applications.

该研究提出了一种新的框架 Heterogeneous Adversarial Neural Domain Adaptation (HANDA)，专门针对异构环境下的域自适应问题，通过统一的神经网络架构实现特征和分布的对齐以及域不变性，并在主要的图像和文本电子商务基准测试中表现出统计显著的预测性能提高。

基于对抗神经表示学习的异构域自适应：以电子商务和网络安全为例的实验

Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity

We present an algorithm that learns representations which explicitly
compensate for domain mismatch and which can be efficiently realized as linear
classifiers. Specifically, we form a linear transformation that maps features
from the target (test) domain to the source (training) domain as part of
training the classifier. We optimize both the transformation and classifier
parameters jointly, and introduce an efficient cost function based on
misclassification loss. Our method combines several features previously
unavailable in a single algorithm: multi-class adaptation through
representation learning, ability to map across heterogeneous feature spaces,
and scalability to large datasets. We present experiments on several image
datasets that demonstrate improved accuracy and computational advantages
compared to previous approaches.

本文介绍了一种能够有效地实现为线性分类器的学习表示算法，该算法能够明确地补偿域不匹配，并在训练分类器的过程中形成从目标（测试）域到源（训练）域的线性转换。通过多类别适应的表示学习，能够在异构特征空间之间进行映射，并且可以扩展到大型数据集。在几个图像数据集上的实验结果表明，与之前的方法相比，提高了准确性并具有计算上的优势。