We introduce a new optimization algorithm, termed contrastive adjustment, for learning Markov transition kernels whose stationary distribution matches the data distribution. Contrastive adjustment is not restricted to a particular family of transition distributions and can be used to model data in both continuous and discrete state spaces. Inspired by recent work on noise-annealed sampling, we propose a particular transition operator, the noise kernel, that can trade mixing speed for sample fidelity. We show that contrastive adjustment is highly valuable in human-computer design processes, as the stationarity of the learned Markov chain enables local exploration of the data manifold and makes it possible to iteratively refine outputs by human feedback. We compare the performance of noise kernels trained with contrastive adjustment to current state-of-the-art generative models and demonstrate promising results on a variety of image synthesis tasks.

翻译：我们提出了一种新的优化算法，称为对比调整，用于学习平稳分布与数据分布匹配的马尔可夫转移核。对比调整不局限于特定的转移分布族，可适用于连续状态空间和离散状态空间中的数据建模。受近期噪声退火采样研究的启发，我们提出了一种特定的转移算子——噪声核（noise kernel），其能在混合速度与样本保真度之间实现权衡。研究表明，对比调整在人机协同设计过程中具有重要价值：由于学习得到的马尔可夫链具有平稳性，该方法能够实现数据流形的局部探索，并支持通过人类反馈对输出结果进行迭代优化。我们将经对比调整训练的噪声核与当前最先进的生成模型进行性能比较，并在多种图像合成任务中展示了具有竞争力的结果。