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Abstract: Traditional deep learning methods have achieved remarkable success by leveraging large-scale labeled datasets. However, in real-world applications, acquiring labeled data is often expensive, ...

Abstract: 1D, 2D and multidimensional convolutions are basic tools in deep learning, notably in convolutional neural networks (CNNs) and in computer vision (template matching, correlation trackers).

We conduct experiments on two publicly available datasets: [Dataset 1 Name] and [Dataset 2 Name]. We summarize the statistics of two datasets in Table [X]. In the data preprocessing step, we remove ...

In this section, we propose [Method Name], which is a [general description of method type] for [purpose or application]. The overall architecture of [Method Name] is shown in ...