Modern industrial fault diagnosis tasks often face the combined challenge of
distribution discrepancy and bi-imbalance. Existing domain adaptation
approaches pay little attention to the prevailing bi-imbalance, leading to poor
domain adaptation performance or even negative transfer. In this work, we
propose a self-degraded contrastive domain adaptation (Sd-CDA) diagnosis
framework to handle the domain discrepancy under the bi-imbalanced data. It
first pre-trains the feature extractor via imbalance-aware contrastive learning
based on model pruning to learn the feature representation efficiently in a
self-supervised manner. Then it forces the samples away from the domain
boundary based on supervised contrastive domain adversarial learning
(SupCon-DA) and ensures the features generated by the feature extractor are
discriminative enough. Furthermore, we propose the pruned contrastive domain
adversarial learning (PSupCon-DA) to pay automatically re-weighted attention to
the minorities to enhance the performance towards bi-imbalanced data. We show
the superiority of the proposed method via two experiments.

通过自我降级的对比域自适应 (Sd-CDA) 诊断框架来处理不平衡数据下的域差异问题，首先通过基于模型剪枝的不平衡感知对比学习方法对特征提取器进行预训练，以自监督方式高效地学习特征表示；然后基于监督对比域对抗学习 (SupCon-DA) 强制将样本远离域边界，确保特征生成器生成的特征具有足够的区分性；此外，提出了剪枝对比域对抗学习 (PSupCon-DA)，自动关注少数类以增强对不平衡数据的性能；通过两个实验展示了所提方法的优越性。

自我退化的对比域自适应用于工业故障诊断中的双失衡数据

Self-degraded contrastive domain adaptation for industrial fault  diagnosis with bi-imbalanced data

Question generation has recently shown impressive results in customizing
question answering (QA) systems to new domains. These approaches circumvent the
need for manually annotated training data from the new domain and, instead,
generate synthetic question-answer pairs that are used for training. However,
existing methods for question generation rely on large amounts of synthetically
generated datasets and costly computational resources, which render these
techniques widely inaccessible when the text corpora is of limited size. This
is problematic as many niche domains rely on small text corpora, which
naturally restricts the amount of synthetic data that can be generated. In this
paper, we propose a novel framework for domain adaptation called contrastive
domain adaptation for QA (CAQA). Specifically, CAQA combines techniques from
question generation and domain-invariant learning to answer out-of-domain
questions in settings with limited text corpora. Here, we train a QA system on
both source data and generated data from the target domain with a contrastive
adaptation loss that is incorporated in the training objective. By combining
techniques from question generation and domain-invariant learning, our model
achieved considerable improvements compared to state-of-the-art baselines.

本研究提出了一种名为对比领域自适应问答（CAQA）的新型领域适应框架，该框架结合了问题生成和域不变学习技术，用于在文本语料库受限的情况下回答域外问题，并且相较于现有技术取得了显著的效果改善。