论文标题
基于层次多模式表示的时尚风格的模仿学习
Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation
论文作者
论文摘要
时尚是一种复杂的社会现象。人们从专家或时尚偶像的示范中遵循时尚风格。但是，对于机器代理人，学习模仿示范中的时尚专家可能具有挑战性，尤其是对于具有高维，多模式观察的环境中的复杂风格。关于时尚服装组成的大多数现有研究都利用监督的学习方法模仿样式图标的行为。这些方法遭受分配转移的困扰：由于贪婪地模仿了一些给定的服装演示，因此它可以从一种样式转移到另一种样式的情况下，鉴于微妙的差异。在这项工作中，我们提出了一种对抗性的逆增强学习公式，以在模仿过程中基于层次多模式表示（HM-AIRL）恢复奖励功能。分层联合表示可以更全面地对专家合成的服装演示进行建模，以恢复奖励功能。我们证明了所提出的HM-AIRL模型能够恢复对多模式观察变化的鲁棒奖励功能，从而使我们能够在不同样式之间的显着差异下学习策略。
Fashion is a complex social phenomenon. People follow fashion styles from demonstrations by experts or fashion icons. However, for machine agent, learning to imitate fashion experts from demonstrations can be challenging, especially for complex styles in environments with high-dimensional, multimodal observations. Most existing research regarding fashion outfit composition utilizes supervised learning methods to mimic the behaviors of style icons. These methods suffer from distribution shift: because the agent greedily imitates some given outfit demonstrations, it can drift away from one style to another styles given subtle differences. In this work, we propose an adversarial inverse reinforcement learning formulation to recover reward functions based on hierarchical multimodal representation (HM-AIRL) during the imitation process. The hierarchical joint representation can more comprehensively model the expert composited outfit demonstrations to recover the reward function. We demonstrate that the proposed HM-AIRL model is able to recover reward functions that are robust to changes in multimodal observations, enabling us to learn policies under significant variation between different styles.
