Autoregressive models have achieved impressive results over a wide range of
domains in terms of generation quality and downstream task performance. In the
continuous domain, a key factor behind this success is the usage of quantized
latent spaces (e.g., obtained via VQ-VAE autoencoders), which allow for
dimensionality reduction and faster inference times. However, using existing
pre-trained models to perform new non-trivial tasks is difficult since it
requires additional fine-tuning or extensive training to elicit prompting. This
paper introduces LASS as a way to perform vector-quantized Latent
Autoregressive Source Separation (i.e., de-mixing an input signal into its
constituent sources) without requiring additional gradient-based optimization
or modifications of existing models. Our separation method relies on the
Bayesian formulation in which the autoregressive models are the priors, and a
discrete (non-parametric) likelihood function is constructed by performing
frequency counts over latent sums of addend tokens. We test our method on
images and audio with several sampling strategies (e.g., ancestral, beam
search) showing competitive results with existing approaches in terms of
separation quality while offering at the same time significant speedups in
terms of inference time and scalability to higher dimensional data.

本论文介绍了一种名为 LASS 的矢量量化潜在自回归源分离方法，通过贝叶斯公式和构建离散概率密度函数来实现无需额外微调和修改现有模型即可将输入信号分离成其成分源，该方法在图像和音频分离方面表现出具有竞争力的结果。