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cs.LG2022
Approximate Data Deletion in Generative Models
Zhifeng Kong, Scott Alfeld
Users have the right to have their data deleted by third-party learned systems, as codified by recent legislation such as the General Data Protection Regulation (GDPR) and the Cali…
cs.LG2022
Data Redaction from Pre-trained GANs
Zhifeng Kong, Kamalika Chaudhuri
Large pre-trained generative models are known to occasionally output undesirable samples, which undermines their trustworthiness. The common way to mitigate this is to re-train the…
cs.SD2022
Speech Denoising in the Waveform Domain with Self-Attention
Zhifeng Kong, Wei Ping, Ambrish Dantrey +1
In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-a…