Rectified Factor Networks

Rectified Factor Networks (RFNs) are an unsupervised technique that learns a non-linear, high-dimensional representation of its input. The underlying algorithm has been published in

Rectified Factor Networks, Djork-Arné Clevert, Andreas Mayr, Thomas Unterthiner, Sepp Hochreiter, NIPS 2015.


librfn is implemented in C++ and can be easily integrated in existing code bases. It also contains a high-level Python wrapper for ease of use. The library can run in either CPU or GPU mode. For larger models the GPU mode offers large speedups and is the recommended mode.

Paper, supplement and manual: