Electrocardiograms (ECG), which record the electrophysiological activity of
the heart, have become a crucial tool for diagnosing these diseases. In recent
years, the application of deep learning techniques has significantly improved
the performance of ECG signal classification. Multi-resolution feature
analysis, which captures and processes information at different time scales,
can extract subtle changes and overall trends in ECG signals, showing unique
advantages. However, common multi-resolution analysis methods based on simple
feature addition or concatenation may lead to the neglect of low-resolution
features, affecting model performance. To address this issue, this paper
proposes the Multi-Resolution Mutual Learning Network (MRM-Net). MRM-Net
includes a dual-resolution attention architecture and a feature complementary
mechanism. The dual-resolution attention architecture processes high-resolution
and low-resolution features in parallel. Through the attention mechanism, the
high-resolution and low-resolution branches can focus on subtle waveform
changes and overall rhythm patterns, enhancing the ability to capture critical
features in ECG signals. Meanwhile, the feature complementary mechanism
introduces mutual feature learning after each layer of the feature extractor.
This allows features at different resolutions to reinforce each other, thereby
reducing information loss and improving model performance and robustness.
Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net
significantly outperforms existing methods in multi-label ECG classification
performance. The code for our framework will be publicly available at
this https URL

本论文提出了一种多分辨率相互学习网络（MRM-Net），通过双重分辨率注意力架构和特征互补机制处理心电图（ECG）信号，以提高多标签 ECG 分类性能。

多分辨率互学习网络用于多标签心电图分类

A Multi-Resolution Mutual Learning Network for Multi-Label ECG  Classification

Universal domain adaptation (UniDA) is a practical but challenging problem,
in which information about the relation between the source and the target
domains is not given for knowledge transfer. Existing UniDA methods may suffer
from the problems of overlooking intra-domain variations in the target domain
and difficulty in separating between the similar known and unknown class. To
address these issues, we propose a novel \textbf{Mutual Learning Network
(MLNet)} with neighborhood invariance for UniDA. In our method,
confidence-guided invariant feature learning with self-adaptive neighbor
selection is designed to reduce the intra-domain variations for more
generalizable feature representation. By using the cross-domain mixup scheme
for better unknown-class identification, the proposed method compensates for
the misidentified known-class errors by mutual learning between the closed-set
and open-set classifiers. Extensive experiments on three publicly available
benchmarks demonstrate that our method achieves the best results compared to
the state-of-the-arts in most cases and significantly outperforms the baseline
across all the four settings in UniDA. Code is available at
this https URL

我们提出了一个新颖的具有邻域不变性的相互学习网络方法（MLNet），通过自适应邻居选择和置信度引导的不变性特征学习来减少目标域内的内部变化，通过交叉领域混杂方案进行更好的未知类别识别，互学习闭合集和开放集分类器以弥补已知类别错误的识别。在三个公开基准测试中，广泛的实验证明我们的方法在大多数情况下均取得最佳结果，并且在 UniDA 的四个设置中显著优于基线。

MLNet: 具有邻域不变性的互学习网络用于通用域适应

MLNet: Mutual Learning Network with Neighborhood Invariance for  Universal Domain Adaptation

Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed the
setting of a single source domain, where all the labeled source data come from
the same distribution. However, in practice the labeled data can come from
multiple source domains with different distributions. In such scenarios, the
single source domain adaptation methods can fail due to the existence of domain
shifts across different source domains and multi-source domain adaptation
methods need to be designed. In this paper, we propose a novel multi-source
domain adaptation method, Mutual Learning Network for Multiple Source Domain
Adaptation (ML-MSDA). Under the framework of mutual learning, the proposed
method pairs the target domain with each single source domain to train a
conditional adversarial domain adaptation network as a branch network, while
taking the pair of the combined multi-source domain and target domain to train
a conditional adversarial adaptive network as the guidance network. The
multiple branch networks are aligned with the guidance network to achieve
mutual learning by enforcing JS-divergence regularization over their prediction
probability distributions on the corresponding target data. We conduct
extensive experiments on multiple multi-source domain adaptation benchmark
datasets. The results show the proposed ML-MSDA method outperforms the
comparison methods and achieves the state-of-the-art performance.

本文介绍一种新的多源域自适应方法 —— Mutual Learning Network for Multiple Source Domain Adaptation (ML-MSDA)。通过互补学习的框架，该方法将目标域与每个单一源域配对，使用条件对抗性域自适应网络作为分支网络进行训练，并将组合后的多源域和目标域一起作为引导网络进行训练。多个分支网络与引导网络对齐，通过在相应目标数据上通过 JS 分歧正则化约束其预测概率分布来实现互补学习，该方法在多个多源域自适应基准数据集上进行了广泛的实验。结果表明，ML-MSDA 方法优于比较方法，并实现了最新的性能表现。