Generative deep neural networks are widely used for speech synthesis, but
most existing models directly generate waveforms or spectral outputs. Humans,
however, produce speech by controlling articulators, which results in the
production of speech sounds through physical properties of sound propagation.
We introduce the Articulatory Generator to the Generative Adversarial Network
paradigm, a new unsupervised generative model of speech production/synthesis.
The Articulatory Generator more closely mimics human speech production by
learning to generate articulatory representations (electromagnetic
articulography or EMA) in a fully unsupervised manner. A separate pre-trained
physical model (ema2wav) then transforms the generated EMA representations to
speech waveforms, which get sent to the Discriminator for evaluation.
Articulatory analysis suggests that the network learns to control articulators
in a similar manner to humans during speech production. Acoustic analysis of
the outputs suggests that the network learns to generate words that are both
present and absent in the training distribution. We additionally discuss
implications of articulatory representations for cognitive models of human
language and speech technology in general.

通过引入 Articulatory Generator 至 Generative Adversarial Network 范式，我们提出了一种新的无监督生成语音生成 / 合成模型，它通过学习以完全无监督的方式生成关节表征（电磁关节成像术或 EMA）更加贴近于人类语音生成。研究表明，该网络学会了像人类一样在语音生产过程中控制关节，还能够生成训练分布中存在和缺失的词语，因此对人类语言的认知模型和言语技术产生了重要的影响

Articulation GAN：无监督建模关节学习

Articulation GAN: Unsupervised modeling of articulatory learning

Deep supervised neural networks trained to classify objects have emerged as
popular models of computation in the primate ventral stream. These models
represent information with a high-dimensional distributed population code,
implying that inferotemporal (IT) responses are also too complex to interpret
at the single-neuron level. We challenge this view by modelling neural
responses to faces in the macaque IT with a deep unsupervised generative model,
beta-VAE. Unlike deep classifiers, beta-VAE "disentangles" sensory data into
interpretable latent factors, such as gender or hair length. We found a
remarkable correspondence between the generative factors discovered by the
model and those coded by single IT neurons. Moreover, we were able to
reconstruct face images using the signals from just a handful of cells. This
suggests that the ventral visual stream may be optimising the disentangling
objective, producing a neural code that is low-dimensional and semantically
interpretable at the single-unit level.

使用深度非监督生成模型（beta-VAE）对猕猴 IT 中面孔的神经响应进行建模，发现与单个 IT 神经元编码的因素具有显著的对应关系，提示脑的视觉通路可能优化解缠绕目标，产生低维且在单元水平上具有语义解释力的神经编码。

无监督深度学习识别单个颞下皮层神经元内语义解缕

Unsupervised deep learning identifies semantic disentanglement in single  inferotemporal neurons

Despite being robust to small amounts of label noise, convolutional neural
networks trained with stochastic gradient methods have been shown to easily fit
random labels. When there are a mixture of correct and mislabelled targets,
networks tend to fit the former before the latter. This suggests using a
suitable two-component mixture model as an unsupervised generative model of
sample loss values during training to allow online estimation of the
probability that a sample is mislabelled. Specifically, we propose a beta
mixture to estimate this probability and correct the loss by relying on the
network prediction (the so-called bootstrapping loss). We further adapt mixup
augmentation to drive our approach a step further. Experiments on CIFAR-10/100
and TinyImageNet demonstrate a robustness to label noise that substantially
outperforms recent state-of-the-art. Source code is available at
this https URL

本文提出了一种使用 beta 混合模型作为无监督生成模型，实时估计样本被错误标记的概率的方法，并通过 bootstrapping loss 来校正模型。同时，作者还对 mixup 数据增强方法做了进一步优化，实验证明该方法具有比最近最先进的方法更强的标签噪声鲁棒性。