论文标题
通过同时学习，通过自动编码器适应半监督域
Semi-Supervised Domain Adaptation with Auto-Encoder via Simultaneous Learning
论文作者
论文摘要
我们提出了一个新的半监督域适应框架，该框架结合了一种新型的基于自动编码器的域适应模型和同时学习方案，该方案对最先进的域适应模型提供了稳定的改进。我们的框架通过在单个图表上使用具有最佳修改的MMD损耗目标函数的单个图表上的新型同时学习方案来训练源和目标自动编码器，从而保持了强大的分配匹配属性。此外，我们通过将对齐的域不变特征空间从源域转移到目标域来设计半监督分类方法。我们在三个数据集上进行评估，并显示了我们的框架可以有效地解决脆弱的收敛（对抗性）和源源匹配空间和目标特征空间之间的弱分布匹配问题（差异），并具有很高的适应性速度，需要非常低的迭代次数。
We present a new semi-supervised domain adaptation framework that combines a novel auto-encoder-based domain adaptation model with a simultaneous learning scheme providing stable improvements over state-of-the-art domain adaptation models. Our framework holds strong distribution matching property by training both source and target auto-encoders using a novel simultaneous learning scheme on a single graph with an optimally modified MMD loss objective function. Additionally, we design a semi-supervised classification approach by transferring the aligned domain invariant feature spaces from source domain to the target domain. We evaluate on three datasets and show proof that our framework can effectively solve both fragile convergence (adversarial) and weak distribution matching problems between source and target feature space (discrepancy) with a high `speed' of adaptation requiring a very low number of iterations.
