Recently, the standard variational autoencoder has been successfully used to
learn a probabilistic prior over speech signals, which is then used to perform
speech enhancement. Variational autoencoders have then been conditioned on a
label describing a high-level speech attribute (e.g. speech activity) that
allows for a more explicit control of speech generation. However, the label is
not guaranteed to be disentangled from the other latent variables, which
results in limited performance improvements compared to the standard
variational autoencoder. In this work, we propose to use an adversarial
training scheme for variational autoencoders to disentangle the label from the
other latent variables. At training, we use a discriminator that competes with
the encoder of the variational autoencoder. Simultaneously, we also use an
additional encoder that estimates the label for the decoder of the variational
autoencoder, which proves to be crucial to learn disentanglement. We show the
benefit of the proposed disentanglement learning when a voice activity label,
estimated from visual data, is used for speech enhancement.

本文提出了一种适用于变分自动编码器的对抗训练方案，用以将标签与变量区分开来，提高语音增强的性能。通过对比标签估计值和实际值，可以证明该方案的有效性。

变分自编码器的解缠学习在音视频语音增强中的应用

Disentanglement Learning for Variational Autoencoders Applied to  Audio-Visual Speech Enhancement

Synthesizing high resolution photorealistic images has been a long-standing
challenge in machine learning. In this paper we introduce new methods for the
improved training of generative adversarial networks (GANs) for image
synthesis. We construct a variant of GANs employing label conditioning that
results in 128x128 resolution image samples exhibiting global coherence. We
expand on previous work for image quality assessment to provide two new
analyses for assessing the discriminability and diversity of samples from
class-conditional image synthesis models. These analyses demonstrate that high
resolution samples provide class information not present in low resolution
samples. Across 1000 ImageNet classes, 128x128 samples are more than twice as
discriminable as artificially resized 32x32 samples. In addition, 84.7% of the
classes have samples exhibiting diversity comparable to real ImageNet data.

本文介绍了改进 GAN 训练的新方法，构建了一种包含标签条件的 GAN 变体来生成 128x128 分辨率的、具有全局一致性的照片级别合成图像，并展示了这些高分辨率样本比低分辨率样本具有更高的可区分性和多样性。