**Domain-Symmetric Network**, or **SymmNet**, is an algorithm for unsupervised multi-class domain adaptation. It features an adversarial strategy of domain confusion and discrimination.

**Adversarial-Learned Loss for Domain Adaptation** is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.

ALDA

Adversarial-Learned Loss for Domain Adaptation

SymmNet

Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

**BTmPG**, or **Back-Translation guided multi-round Paraphrase Generation**, is a multi-round paraphrase generation method that leverages back-translation to guide paraphrase model during training and generates paraphrases in a multiround process. The model regards paraphrase generation as a monolingual translation task. Given a paraphrase pair $\left(S\_{0}, P\right)$, which $S\_{0}$ is the original/source sentence and $P$ is the target paraphrase given in the dataset. In the first round generation, we send $S\_{0}$ into a paraphrase model to generate a paraphrase $S\_{1}$. In the second round generation, we use the $S\_{1}$ as the input of the model to generate a new paraphrase $S\_{2}$. And so forth, in the $i$-th round generation, we send $S\_{i−1}$ into the paraphrase model to generate $S\_{i}$.
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Source	Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com