Artificial neural networks, celebrated for their human-like cognitive
learning abilities, often encounter the well-known catastrophic forgetting (CF)
problem, where the neural networks lose the proficiency in previously acquired
knowledge. Despite numerous efforts to mitigate CF, it remains the significant
challenge particularly in complex changing environments. This challenge is even
more pronounced in cross-domain adaptation following the continual learning
(CL) setting, which is a more challenging and realistic scenario that is
under-explored. To this end, this article proposes a cross-domain CL approach
making possible to deploy a single model in such environments without
additional labelling costs. Our approach, namely continual learning approach
for many processes (CLAMP), integrates a class-aware adversarial domain
adaptation strategy to align a source domain and a target domain. An
assessor-guided learning process is put forward to navigate the learning
process of a base model assigning a set of weights to every sample controlling
the influence of every sample and the interactions of each loss function in
such a way to balance the stability and plasticity dilemma thus preventing the
CF problem. The first assessor focuses on the negative transfer problem
rejecting irrelevant samples of the source domain while the second assessor
prevents noisy pseudo labels of the target domain. Both assessors are trained
in the meta-learning approach using random transformation techniques and
similar samples of the source domain. Theoretical analysis and extensive
numerical validations demonstrate that CLAMP significantly outperforms
established baseline algorithms across all experiments by at least $10\%$
margin.

我们在这篇文章中提出了一种名为 CLAMP 的跨领域持续学习方法，它集成了一种类感知对抗领域自适应策略，以在复杂变化环境中实现单一模型的部署，避免了额外的标注成本，并且在理论分析和大量实验证明中取得了至少 10% 的显著性优于现有基准算法的效果。

跨领域连续学习的 CLAMP 方法

Cross-Domain Continual Learning via CLAMP

WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT),
featuring 1010 curated lead sheets in ABC notation. To expand application
scenarios of WikiMT, we add both objective (album, lyrics, video) and
subjective emotion (12 emotion adjectives) and emo\_4q (Russell 4Q) attributes,
enhancing its usability for music information retrieval, conditional music
generation, automatic composition, and emotion classification, etc.
Additionally, CLaMP is implemented to correct the attributes inherited from
WikiMT to reduce errors introduced during original data collection and enhance
the accuracy and completeness of our dataset.

WikiMT++ 是 WikiMusicText (WikiMT) 的扩展和改进版本，包括 1010 个精选的 ABC 符号音乐简谱。为了增加 WikiMT 的应用场景，我们添加了客观属性（专辑、歌词、视频）和主观情感（12 个情感形容词）以及 emo_4q (Russell 4Q) 属性，增强了它在音乐信息检索、条件音乐生成、自动作曲和情感分类等方面的可用性。此外，我们实施了 CLaMP 来纠正从 WikiMT 继承而来的属性，以减少原始数据收集中引入的错误，并提高数据集的准确性和完整性。