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Deep learning has achieved great success in the challenging circuitannotation task by employing Convolutional Neural Networks (CNN) for thesegmentation of circuit structures. The deep learning approaches require alarge amount of manually annotated training data to achieve a good performance,which could cause a degradation in performance if a deep learning model trainedon a given dataset is applied to a different dataset. This is commonly known asthe domain shift problem for circuit annotation, which stems from the possiblylarge variations in distribution across different image datasets. The differentimage datasets could be obtained from different devices or different layerswithin a single device. To address the domain shift problem, we proposeHistogram-gated Image Translation (HGIT), an unsupervised domain adaptationframework which transforms images from a given source dataset to the domain ofa target dataset, and utilize the transformed images for training asegmentation network. Specifically, our HGIT performs generative adversarialnetwork (GAN)-based image translation and utilizes histogram statistics fordata curation. Experiments were conducted on a single labeled source datasetadapted to three different target datasets (without labels for training) andthe segmentation performance was evaluated for each target dataset. We havedemonstrated that our method achieves the best performance compared to thereported domain adaptation techniques, and is also reasonably close to thefully supervised benchmark.

Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis