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Domain adaption is a special transfer learning method, whose source domain and target domain generally have different data distribution, but need to complete the same task. There have been many significant types of research on domain adaptation in 2D images, but in 3D data processing, domain adaptation is still in its infancy. Therefore, we design a novel domain adaptive network to complete the unsupervised point cloud classification task. Specifically, we propose a multi-scale transform module to improve the feature extractor. Besides, a spatial-awareness attention module combined with channel attention to assign weights to each node is designed to represent hierarchically scaled features. We have validated the proposed method on the PointDA-10 dataset for domain adaption classification tasks. Empirically, it shows strong performance on par or even better than state-of-the-art.

Previous studies have already shown that Raman spectroscopy can be used in the encoding of suspension array technology. However, almost all existing convolutional neural network-based decoding approaches rely on supervision with ground truth, and may not be well generalized to unseen datasets, which were collected under different experimental conditions, applying with the same coded material. In this study, we propose an improved model based on CyCADA, named as Detail constraint Cycle Domain Adaptive Model (DCDA). DCDA implements the classification of unseen datasets through domain adaptation, adapts representations at the encode level with decoder-share, and enforces coding features while leveraging a feat loss. To improve detailed structural constraints, DCDA takes downsample connection and skips connection. Our model improves the poor generalization of existing models and saves the cost of the labeling process for unseen target datasets. Compared with other models, extensive experiments and ablation studies show the superiority of DCDA in terms of classification stability and generalization. The model proposed by the research achieves a classification with an accuracy of 100% when applied in datasets, in which the spectrum in the source domain is far less than the target domain.