研究人员通过将多模态能量模型（EBMs）与多模态变分自编码器（VAEs）相结合，开发了一个新的学习框架。该方法解决了现有方法中马尔可夫链蒙特卡洛（MCMC）采样混合效果差以及难以发现模态间关系的问题。所提出的框架将最大似然估计（MLE）更新与数据和潜在空间中的MCMC修正交织在一起，从而能够更有效地采样和学习连贯的多模态数据。 AI
影响 引入了一种改进多模态生成模型训练和样本连贯性的新方法。
排序理由 学术论文，详细介绍了多模态能量模型的新学习框架。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Car…
Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. However, learning multimodal EBM by maximum likelihood requires Markov Chain Monte Carlo (MCMC) sampling in the joint data space, wher…