Unsupervised Domain Adaptation (UDA) aims at improving the generalization
capability of a model trained on a source domain to perform well on a target
domain for which no labeled data is available. In this paper, we consider the
semantic segmentation of urban scenes and we propose an approach to adapt a
deep neural network trained on synthetic data to real scenes addressing the
domain shift between the two different data distributions. We introduce a novel
UDA framework where a standard supervised loss on labeled synthetic data is
supported by an adversarial module and a self-training strategy aiming at
aligning the two domain distributions. The adversarial module is driven by a
couple of fully convolutional discriminators dealing with different domains:
the first discriminates between ground truth and generated maps, while the
second between segmentation maps coming from synthetic or real world data. The
self-training module exploits the confidence estimated by the discriminators on
unlabeled data to select the regions used to reinforce the learning process.
Furthermore, the confidence is thresholded with an adaptive mechanism based on
the per-class overall confidence. Experimental results prove the effectiveness
of the proposed strategy in adapting a segmentation network trained on
synthetic datasets like GTA5 and SYNTHIA, to real world datasets like
Cityscapes and Mapillary.

本文提出一种基于无监督域自适应（UDA）的方法，针对城市场景的语义分割应用领域，将深度神经网络从合成数据源，适应到真实数据源上，通过引入对抗模块和自训练策略实现充分对齐两个数据分布，模块化的架构使得模型适用范围更广，实验结果表明该方法的有效性和稳健性。

多领域判别器与自适应自训练的无监督领域自适应

Unsupervised Domain Adaptation with Multiple Domain Discriminators and  Adaptive Self-Training

Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.

本文提出了一种弱监督文本分类方法，其包括一个利用种子信息生成伪标签文档进行模型预训练的伪文档生成器和一个在真实未标记数据上引导模型改进的自训练模块，具有处理不同类型弱监督任务和易于与深度神经网络模型集成等优点，并在三个真实数据集上进行了广泛的实验，结果表明其大幅优于基准方法而不需要过多的训练数据。