In this work, we propose a flexible method for generating variations of
discrete sequences in which tokens can be grouped into basic units, like
sentences in a text or bars in music. More precisely, given a template
sequence, we aim at producing novel sequences sharing perceptible similarities
with the original template without relying on any annotation; so our problem of
generating variations is intimately linked to the problem of learning relevant
high-level representations without supervision. Our contribution is two-fold:
First, we propose a self-supervised encoding technique, named Vector Quantized
Contrastive Predictive Coding which allows to learn a meaningful assignment of
the basic units over a discrete set of codes, together with mechanisms allowing
to control the information content of these learnt discrete representations.
Secondly, we show how these compressed representations can be used to generate
variations of a template sequence by using an appropriate attention pattern in
the Transformer architecture. We illustrate our approach on the corpus of J.S.
Bach chorales where we discuss the musical meaning of the learnt discrete codes
and show that our proposed method allows to generate coherent and high-quality
variations of a given template.

本文提出了一种灵活的方法，用于在离散序列中生成具有基本单元（例如文本中的句子或音乐中的小节）分组的变化。我们的方法依赖自监督编码技术和 Transformer 架构，通过学习有意义的分配和压缩表示，在不需要任何注释的情况下生成新的序列。实验展示了在巴赫合唱曲集中使用该方法能够生成连贯而高质量的曲目变化。

基于模板的音乐生成的矢量量化对比预测编码

Vector Quantized Contrastive Predictive Coding for Template-based Music  Generation

Deep generative models have been successfully used to learn representations
for high-dimensional discrete spaces by representing discrete objects as
sequences and employing powerful sequence-based deep models. Unfortunately,
these sequence-based models often produce invalid sequences: sequences which do
not represent any underlying discrete structure; invalid sequences hinder the
utility of such models. As a step towards solving this problem, we propose to
learn a deep recurrent validator model, which can estimate whether a partial
sequence can function as the beginning of a full, valid sequence. This
validator provides insight as to how individual sequence elements influence the
validity of the overall sequence, and can be used to constrain sequence based
models to generate valid sequences -- and thus faithfully model discrete
objects. Our approach is inspired by reinforcement learning, where an oracle
which can evaluate validity of complete sequences provides a sparse reward
signal. We demonstrate its effectiveness as a generative model of Python 3
source code for mathematical expressions, and in improving the ability of a
variational autoencoder trained on SMILES strings to decode valid molecular
structures.

我们提出了一种基于深度循环验证模型的方法，以帮助深度生成模型在生成离散对象时可以有效地生成有效序列并模拟离散结构。我们使用强化学习的思想来设计这个模型，通过评估每个元素对序列有效性的影响，对序列模型进行有效约束。我们成功地使用该模型分别生成了 Python 3 中的数学表达式和 SMILES 字符串表示的分子结构。