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Classification models trained on data from one source may underperform when tested on data acquired from different sources due to shifts in data distributions, which limit the models’ generalizability in real-world applications. Domain adaptation methods proposed to align such shifts in source-target data distributions use contrastive learning or adversarial techniques with or without internal cluster alignment. The intracluster alignment is performed using standalone k-means clustering on image embedding. This paper introduces a novel deep clustering approach to align cluster distributions in tandem with adapting source and target data distributions. Our method learns and aligns a mixture of cluster distributions in the unlabeled target domain with those in the source domain in a unified deep representation learning framework. Experiments demonstrate that intra-cluster alignment improves classification accuracy in nine out of ten domain adaptation examples. These improvements range between 0.3% and 2.0% compared to k-means clustering of embedding and between 0.4% and 5.8% compared to methods without class-level alignment. Unlike current domain adaptation methods, the proposed cluster distribution-based deep learning provides a quantitative and explainable measure of distribution shifts in data domains. We have publicly shared the source code for the algorithm implementation. 

Abstract: Surface acoustic wave (SAW) sensors with increasingly unique and refined designed patterns are often developed using the lithographic fabrication processes. Emerging applications of SAW sensors often require novel materials, which may present uncharted fabrication outcomes. The fidelity of the SAW sensor performance is often correlated with the ability to restrict the presence of defects in post-fabrication. Therefore, it is critical to have effective means to detect the presence of defects within the SAW sensor. However, labor-intensive manual labeling is often required due to the need for precision identification and classification of surface features for increased confidence in model accuracy. One approach to automating defect detection is to leverage effective machine learning techniques to analyze and quantify defects within the SAW sensor. In this paper, we propose a machine learning approach using a deep convolutional autoencoder to segment surface features semantically. The proposed deep image autoencoder takes a grayscale input image and generates a color image segmenting the defect region in red, metallic interdigital transducing (IDT) fingers in green, and the substrate region in blue. Experimental results demonstrate promising segmentation scores in locating the defects and regions of interest for a novel SAW sensor variant. The proposed method can automate the process of localizing and measuring post-fabrication defects at the pixel level that may be missed by error-prone visual inspection.