In this paper, we propose conditional adversarial networks (CANs), a
framework that explores the relationship between the shared features and the
label predictions to impose more discriminability to the shared features, for
multi-domain text classification (MDTC). The proposed CAN introduces a
conditional domain discriminator to model the domain variance in both shared
feature representations and class-aware information simultaneously and adopts
entropy conditioning to guarantee the transferability of the shared features.
We provide theoretical analysis for the CAN framework, showing that CAN's
objective is equivalent to minimizing the total divergence among multiple joint
distributions of shared features and label predictions. Therefore, CAN is a
theoretically sound adversarial network that discriminates over multiple
distributions. Evaluation results on two MDTC benchmarks show that CAN
outperforms prior methods. Further experiments demonstrate that CAN has a good
ability to generalize learned knowledge to unseen domains.

本文提出了条件对抗网络 (CANs) 框架，旨在探索共享特征和标签预测之间的关系，以对共享特征施加更多的可辨识性，用于多领域文本分类 (MDTC)。CAN 引入了一个条件域鉴别器，同时对共享特征表示和类别感知信息建模，采用熵条件保证共享特征的可转移性。我们提供了 CAN 框架的理论分析，证明 CAN 的目标等价于最小化共享特征和标签预测的多个联合分布之间的总发散。因此，CAN 是一个理论上良好的对抗网络，其可以判别多个分布。在两个 MDTC 基准测试上的评估结果表明，CAN 优于之前的方法。进一步的实验表明，CAN 具有将学习到的知识推广到未见领域的良好能力。

多域文本分类的条件对抗网络

Conditional Adversarial Networks for Multi-Domain Text Classification

There is an increasing interest on accelerating neural networks for real-time
applications. We study the student-teacher strategy, in which a small and fast
student network is trained with the auxiliary information learned from a large
and accurate teacher network. We propose to use conditional adversarial
networks to learn the loss function to transfer knowledge from teacher to
student. The proposed method is particularly effective for relatively small
student networks. Moreover, experimental results show the effect of network
size when the modern networks are used as student. We empirically study the
trade-off between inference time and classification accuracy, and provide
suggestions on choosing a proper student network.

本文介绍了使用条件对抗网络来学习从大型精准教师神经网络中获取知识来训练小型快速学生神经网络的学生 - 教师策略，并研究了网络大小对于分类准确度和推理时间之间的平衡的影响和建议。

通过条件对抗网络进行知识蒸馏，训练浅层和瘦身网络以实现加速

Training Shallow and Thin Networks for Acceleration via Knowledge  Distillation with Conditional Adversarial Networks

We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems that traditionally would require very different loss formulations.
We demonstrate that this approach is effective at synthesizing photos from
label maps, reconstructing objects from edge maps, and colorizing images, among
other tasks. Indeed, since the release of the pix2pix software associated with
this paper, a large number of internet users (many of them artists) have posted
their own experiments with our system, further demonstrating its wide
applicability and ease of adoption without the need for parameter tweaking. As
a community, we no longer hand-engineer our mapping functions, and this work
suggests we can achieve reasonable results without hand-engineering our loss
functions either.

我们研究了有条件的对抗性网络作为图像转换问题的通用解决方案，并展示了这种方法在从标签地图中合成照片，从边缘地图中重建对象和彩色化图像等任务中的有效性。