# Neural separation of observed and unobserved distributions

arXiv: https://arxiv.org/abs/1811.12739 · HTML: https://mlsanity.com/paper/1811.12739/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics](https://mlsanity.com/paper/2105.08164/) (2021)
- [Blind Image Decomposition](https://mlsanity.com/paper/2108.11364/) (2021)
- [Detector-Free Weakly Supervised Grounding by Separation](https://mlsanity.com/paper/2104.09829/) (2021)
- [Latent Autoregressive Source Separation](https://mlsanity.com/paper/2301.08562/) (2023)
- [Mixed X-Ray Image Separation for Artworks with Concealed Designs](https://mlsanity.com/paper/2201.09167/) (2022)
- [Adversarial Generative NMF for Single Channel Source Separation](https://mlsanity.com/paper/2305.01758/) (2023)

## Abstract (arXiv)

Separating mixed distributions is a long standing challenge for machine learning and signal processing. Most current methods either rely on making strong assumptions on the source distributions or rely on having training samples of each source in the mixture. In this work, we introduce a new method---Neural Egg Separation---to tackle the scenario of extracting a signal from an unobserved distribution additively mixed with a signal from an observed distribution. Our method iteratively learns to separate the known distribution from progressively finer estimates of the unknown distribution. In some settings, Neural Egg Separation is initialization sensitive, we therefore introduce Latent Mixture Masking which ensures a good initialization. Extensive experiments on audio and image separation tasks show that our method outperforms current methods that use the same level of supervision, and often achieves similar performance to full supervision.