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Neural Separation of Observed and Unobserved Distributions
Tavi Halperin · Ariel Ephrat · Yedid Hoshen

Thu Jun 13 10:00 AM -- 10:05 AM (PDT) @ Room 201

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.

Author Information

Tavi Halperin (Hebrew University of Jerusalem)
Ariel Ephrat (HUJI)
Yedid Hoshen (Hebrew University of Jerusalem and Facebook AI Research)

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