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Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection
In this paper, we propose a convolutional recurrent reconstructive network (CRRN), which decomposes the anomaly patterns generated by the printer defects, from SPI data.
Dense Recurrent Neural Network with Attention Gate
We propose the dense RNN, which has the fully connections from each hidden state to multiple preceding hidden states of all layers directly.
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