B-DENSE：密集ense网络监督效率的分支方法
B-DENSE: Branching For Dense Ensemble Network Supervision Efficiency
AI总结：
B-DENSE通过多分支轨迹对齐方法，提升密集ense网络的监督效率，实现更高质量的图像生成。
摘要：
受非平衡热力学启发，扩散模型在生成建模中取得了最先进的性能。然而，其迭代采样性质导致推理延迟高。尽管最近的蒸馏技术加速了采样，但它们丢弃了中间轨迹步骤。这种稀疏监督导致了结构信息的损失，并引入了显著的离散化误差。为缓解这一问题，我们提出了B-DENSE，一种新的框架，利用多分支轨迹对齐。我们修改学生架构以输出K倍扩展的通道，其中每个子集对应一个特定的分支，代表教师轨迹中的离散中间步骤。通过训练这些分支同时映射到教师目标时间步的整个序列，我们强制密集中间轨迹对齐。因此，学生模型在训练的早期阶段学习导航解空间，相较于基线蒸馏框架，展现出更优异的图像生成质量。
英文摘要：
Inspired by non-equilibrium thermodynamics, diffusion models have achieved state-of-the-art performance in generative modeling. However, their iterative sampling nature results in high inference latency. While recent distillation techniques accelerate sampling, they discard intermediate trajectory steps. This sparse supervision leads to a loss of structural information and introduces significant discretization errors. To mitigate this, we propose B-DENSE, a novel framework that leverages multi-branch trajectory alignment. We modify the student architecture to output $K$-fold expanded channels, where each subset corresponds to a specific branch representing a discrete intermediate step in the teacher's trajectory. By training these branches to simultaneously map to the entire sequence of the teacher's target timesteps, we enforce dense intermediate trajectory alignment. Consequently, the student model learns to navigate the solution space from the earliest stages of training, demonstrating superior image generation quality compared to baseline distillation frameworks.
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。
