论文标题
超越多个实例神经网络：基于本地模式聚合的深度学习模型
Go Beyond Multiple Instance Neural Networks: Deep-learning Models based on Local Pattern Aggregation
论文作者
论文摘要
深度卷积神经网络（CNN）在处理临床心电图（ECG），扬声器独立的语音和复杂图像方面带来了突破。但是，典型的CNN需要固定的输入大小，而在实际使用中处理可变大小的数据是常见的。诸如长期记忆（LSTM）之类的经常性网络能够消除限制，但具有很高的计算复杂性。在本文中，我们提出了基于本地模式聚合的深度学习模型，以有效解决这两个问题。新型的网络结构称为LPANET，嵌入了种植和聚集操作。有了这些新功能，LPANET可以减少调整模型参数的难度，从而倾向于提高泛化性能。为了证明有效性，我们将其应用于过早的心室收缩检测问题，实验结果表明，与CNN和LSTM等经典网络模型相比，我们提出的方法具有某些优势。
Deep convolutional neural networks (CNNs) have brought breakthroughs in processing clinical electrocardiograms (ECGs), speaker-independent speech and complex images. However, typical CNNs require a fixed input size while it is common to process variable-size data in practical use. Recurrent networks such as long short-term memory (LSTM) are capable of eliminating the restriction, but suffer from high computational complexity. In this paper, we propose local pattern aggregation-based deep-learning models to effectively deal with both problems. The novel network structure, called LPANet, has cropping and aggregation operations embedded into it. With these new features, LPANet can reduce the difficulty of tuning model parameters and thus tend to improve generalization performance. To demonstrate the effectiveness, we applied it to the problem of premature ventricular contraction detection and the experimental results shows that our proposed method has certain advantages compared to classical network models, such as CNN and LSTM.
