A convolutional recurrent autoencoder with a spatiotemporal memory cell and attention mechanism detects solder paste printing defects from reconstruction errors, with F1 gains over a statistical baseline but near-equal to a ConvLSTM autoencoder.
Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,
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Convolutional Recurrent Reconstructive Network for Spatiotemporal Anomaly Detection in Solder Paste Inspection
A convolutional recurrent autoencoder with a spatiotemporal memory cell and attention mechanism detects solder paste printing defects from reconstruction errors, with F1 gains over a statistical baseline but near-equal to a ConvLSTM autoencoder.