新方法让生成模型更稳定地匹配数据分布。
Distribution Matching via Generalized Consistency Models
- 基于连续归一化流的稳定性思想，设计新型分布匹配目标
- 理论证明目标函数可有效避免训练不稳定问题
- 适用于跨域翻译与适配等任务，适合关注生成模型稳定的读者
生成模型在多种数据模态中表现卓越。除数据合成外，它们在潜在变量建模、域迁移和域适应等分布匹配任务中也至关重要。生成对抗网络（GAN）因能处理高维数据并灵活施加约束，成为主流选择。但其双层极小极大优化目标常导致训练困难，且易出现模式崩溃。本文受连续归一化流（CNF）中一致性模型启发，提出一种新型分布匹配方法。该模型继承了CNF的简洁范数最小化目标，同时保持类似GAN的约束适应能力。我们提供了所提目标的理论验证，并在合成与真实世界数据集上展示了其性能。
原文摘要 · Abstract (English)
Recent advancement in generative models have demonstrated remarkable performance across various data modalities. Beyond their typical use in data synthesis, these models play a crucial role in distribution matching tasks such as latent variable modeling, domain translation, and domain adaptation. Generative Adversarial Networks (GANs) have emerged as the preferred method of distribution matching due to their efficacy in handling high-dimensional data and their flexibility in accommodating various constraints. However, GANs often encounter challenge in training due to their bi-level min-max optimization objective and susceptibility to mode collapse. In this work, we propose a novel approach for distribution matching inspired by the consistency models employed in Continuous Normalizing Flow (CNF). Our model inherits the advantages of CNF models, such as having a straight forward norm minimization objective, while remaining adaptable to different constraints similar to GANs. We provide theoretical validation of our proposed objective and demonstrate its performance through experiments on synthetic and real-world datasets.
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