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However, since autoencoder adopts the bottleneck layer to reconstruct data, it is hard to control its generalization capability. When the generalization capability is high, anomalous features can be ...

The labelled and unlabelled datasets are used to train the autoencoder. The labelled dataset is used to train the classifier. The test dataset is used to validate the trained classifier. The following ...

This paper proposes a learning-based approach for reconstruction of global illumination with very low sampling budgets (as low as 1 spp) at interactive rates. At 1 sample per pixel (spp), the Monte ...

By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani ...

Image reconstruction-based methods with autoencoder have been widely used for unsupervised anomaly detection. By training the reconstruction on normal samples, Dual-Constraint Autoencoder and Adaptive ...