论文标题
层次分支的扩散模型利用类条件生成的数据集结构
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
论文作者
论文摘要
类标签的数据集，尤其是科学领域中常见的数据集，充满了内部结构，但是当前的班级条件扩散模型忽略了这些关系，并以平坦的方式隐含地对所有类别进行了分散。为了利用这种结构，我们提出了层次分支的扩散模型，作为阶级条件生成的新框架。分支扩散模型依赖于与传统模型相同的扩散过程，但分别了解层次结构的每个分支的反向扩散。我们强调了分支扩散模型的几个优势，而不是当前的班级条件扩散方法，包括在持续学习环境中扩展到新的类，是基于类比的条件生成的更复杂形式的形式（即传播），以及对生成过程的新颖解释性。我们广泛评估了跨越许多数据模式的几个基准和大型现实科学数据集上的分支扩散模型。
Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships and implicitly diffuse on all classes in a flat fashion. To leverage this structure, we propose hierarchically branched diffusion models as a novel framework for class-conditional generation. Branched diffusion models rely on the same diffusion process as traditional models, but learn reverse diffusion separately for each branch of a hierarchy. We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i.e. transmutation), and a novel interpretability into the generation process. We extensively evaluate branched diffusion models on several benchmark and large real-world scientific datasets spanning many data modalities.
