Idealised as universal approximators, learners such as neural networks can be viewed as "variable functions" that may become one of a range of concrete functions after training. In the same way that equations constrain the possible values of variables in algebra, we may view objective functions as constraints on the behaviour of learners. We extract the equivalences perfectly optimised objective functions impose, calling them "tasks". For these tasks, we develop a formal graphical language that allows us to: (1) separate the core tasks of a behaviour from its implementation details; (2) reason about and design behaviours model-agnostically; and (3) simply describe and unify approaches in machine learning across domains. As proof-of-concept, we design a novel task that enables converting classifiers into generative models we call "manipulators", which we implement by directly translating task specifications into code. The resulting models exhibit capabilities such as style transfer and interpretable latent-space editing, without the need for custom architectures, adversarial training or random sampling. We formally relate the behaviour of manipulators to GANs, and empirically demonstrate their competitive performance with VAEs. We report on experiments across vision and language domains aiming to characterise manipulators as approximate Bayesian inversions of discriminative classifiers.

通过设计一种新的任务模型，利用神经网络和目标函数，实现了一种能够将分类器转换为生成模型的新方法，称为“操作器”。通过直接将任务规范转换成代码，操作器展示了风格转换和可解释的潜在空间编辑等功能，无需使用自定义架构、对抗训练或随机抽样。并与生成对抗网络（GANs）进行了形式化的关系分析，并通过实证研究证明了操作器与变分自编码器（VAEs）在性能上的竞争力。

机器学习任务的模式语言