For an autoencoder anomaly detection system, model overfitting is characterized by a situation where all reconstructed inputs match the source inputs very closely, and therefore all reconstruction ...

Second, we use a second autoencoder to enhance the data representation of the reconstructed rating matrix, which can alleviate the loss of some key feature information during reconstruction.

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 ...

The encoder effectively takes the returns and tries to encode them in the model, which is then used for reconstructing the returns.” “In the reconstruction ratio, the autoencoder takes the place of ...

We propose an unsupervised method for detecting adversarial attacks in inner layers of autoencoder (AE) networks by maximizing a non-parametric measure of anomalous node activations.

Detecting changes in asset co-movement using the autoencoder reconstruction ratio ARR aims to anticipate volatility patterns to provide signals for risk management and trading ...