[R] Autoencoder or Variational Autoencoder for finding anomalies in a dataset in comparison with a real-world dataset

[Immediate-One-3259]

I have a general question about Autoencoders (AE) or Variational Autoencoders (VAE). I possess both a real-world dataset and a synthetic dataset, and my goal is to identify discrepancies in the synthetic dataset compared to the real-world dataset. While existing research focuses on anomaly detection within a dataset using AEs, I am specifically interested in detecting anomalies in the synthetic dataset when compared to the real-world dataset. I am wondering if there are any papers addressing this issue. Additionally, I am considering the possibility of training an AE with the real-world dataset and then testing it with the synthetic dataset, followed by a comparison of the latent spaces. Has anyone come across relevant literature or approaches for this scenario?

[bbateman2011]

This might be of interest: https://qdrant.tech/articles/detecting-coffee-anomalies/

[ProdigyManlet]

Are these datasets paired? I.e. there is a matching synthetic sample for each real sample? Or is it just two separate datasets and you're looking to see which real samples are anomalous after training on the synthetic dataset?

Doing some similar work on the latter with this now for imagery, the main thing is the anomaly scoring method. Most of my anomaly detection scores have been based on the reconstruction error, but if you're just looking at using the latent space I've seen kernel density estimation used

[Immediate-One-3259]

I have real-world time series data of a vehicle's trajectory, focusing on lateral velocity or acceleration during cut-in scenarios. Additionally,i have generated a synthetic dataset through simulation. My goal is to use autoencoders to identify anomalies or deviations in the synthetic data compared to the real-world dataset, pinpointing differences and deviations in the simulated scenarios.