Reproducible, incremental representation learning with Rosetta VAE. (arXiv:2201.05206v1 [cs.LG])

Variational autoencoders are among the most popular methods for distilling
low-dimensional structure from high-dimensional data, making them increasingly
valuable as tools for data exploration and scientific discovery. However,
unlike typical machine learning problems in which a single model is trained
once on a single large dataset, scientific workflows privilege learned features
that are reproducible, portable across labs, and capable of incrementally
adding new data. Ideally, methods used by different research groups should
produce comparable results, even without sharing fully trained …

arxiv incremental learning