一种马尔可夫随机场多模态变分自编码器
A Markov Random Field Multi-Modal Variational AutoEncoder
AI总结：
本文提出将马尔可夫随机场引入先验与后验分布的多模态VAE，以更有效建模模态间复杂交互，并在PolyMNIST及合成数据集上验证了其竞争力与优越性。
摘要：
近年来，多模态变分自编码器（VAEs）的进展凸显了其在建模来自多个模态的复杂数据方面的潜力。然而，许多现有方法使用相对简单的聚合方案，可能无法充分捕捉不同模态之间存在的复杂动态。本工作提出了一种新颖的多模态VAE，将马尔可夫随机场（MRF）同时引入先验分布和后验分布。这一整合旨在更有效地捕捉复杂的模态间交互。与以往模型不同，我们的方法专门设计用于建模和利用这些关系的复杂性，从而能够更忠实地表示多模态数据。我们的实验表明，该模型在标准PolyMNIST数据集上具有竞争力的表现，并在一个专门设计的、用于测试复杂关系的合成数据集上，在管理复杂模态间依赖方面展现出优越性能。
英文摘要：
Recent advancements in multimodal Variational AutoEncoders (VAEs) have highlighted their potential for modeling complex data from multiple modalities. However, many existing approaches use relatively straightforward aggregating schemes that may not fully capture the complex dynamics present between different modalities. This work introduces a novel multimodal VAE that incorporates a Markov Random Field (MRF) into both the prior and posterior distributions. This integration aims to capture complex intermodal interactions more effectively. Unlike previous models, our approach is specifically designed to model and leverage the intricacies of these relationships, enabling a more faithful representation of multimodal data. Our experiments demonstrate that our model performs competitively on the standard PolyMNIST dataset and shows superior performance in managing complex intermodal dependencies in a specially designed synthetic dataset, intended to test intricate relationships.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
