Create an LSTM-based Autoencoder model to learn a representation of these signals. Detect anomalous segments (potentially high-stress signals) by computing reconstruction errors. Note on Training ...

The autoencoder was trained to reconstruct background-only scenes. Ships, which were never seen during training, appear as anomalies in the reconstruction due to their deviation from the learned ...

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In ...

Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus on learning patch ...