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:我们引入了一种新的优化算法,称为对比调整,用于学习马尔科夫转移核,其平稳分布匹配数据分布。对比调整不限于特定族的转移分布,可用于建模连续和离散状态空间中的数据。受最近关于噪声退火抽样的工作启发,我们提出了特定的转移操作符噪声核,它可以在样本保真度和混合速度之间进行权衡。我们展示了对比调整在人机设计过程中具有极高的使用价值,因为所学习的马尔科夫链的平稳性使得可以对数据流形进行局部探索,并通过人类反馈迭代地改进输出。我们将通过对比调整训练的噪声核与当前最先进的生成模型进行比较,并证明了在各种图像合成任务上的有希望的结果。