Generative Adversarial Source Separation
收藏DataCite Commons2026-01-07 更新2025-04-16 收录
下载链接：
https://service.tib.eu/ldmservice/dataset/6de1bb26-9108-4908-8947-c7ad7440f2ac
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资源简介：
Generative source separation methods such as non-negative matrix factorization (NMF) or auto-encoders, rely on the assumption of an output probability density. Generative Adversarial Networks (GANs) can learn data distributions without needing a parametric assumption on the output density.
提供机构：
TIB
创建时间：
2025-01-03
