## Introduction Deep learning models have become increasingly popular for their ability to analyze large amounts of data. However, a fundamental limitation of deep learning models in high-dimensional tabular domains is the 'generalization collapse': models learn precise decision boundaries for known distributions but fail catastrophically when facing out-of-distribution data. Researchers have proposed a new approach called Latent Sculpting that uses manifold learning to create a more robust model. ## How it works The Latent Sculpting approach consists of two stages: the first stage uses a representation learning framework that 'sculpts' the data into a low-entropy, hyperspherical cluster. The second stage conditions a Masked Autoregressive Flow (MAF) on this pre-structured manifold to learn an exact density estimate. ## Results The results show that the Latent Sculpting approach is able to detect anomalies in unseen data with an accuracy of 87%. This is particularly significant because traditional deep learning models have difficulty detecting anomalies in unseen data. ## Conclusion Researchers hope that the Latent Sculpting approach can be used to create more robust and capable deep learning models.