想提升扩散模型的表示质量吗？这篇论文研究了训练中的重构与解耦权衡，还教你用门控残差U-Net和噪声课程来引导优化，效果不错。
论文研究了扩散自编码器在训练中优化轨迹的不同机制，发现模型会进入重构优先或解耦优先两个早期阶段。通过分析图像重建与表示质量之间的曲线，作者提出可以通过控制U-Net捷径路径和噪声级暴露来调节重构-解耦权衡。提出的SteeringDRL方法采用门控残差U-Net和噪声级课程训练，在多个解耦基准上提升了表示质量并降低了种子敏感性。该方法还扩展到目标中心学习中的空间解耦，在合成和真实数据集上改善了分割质量。
Steering Optimisation Trajectories in Diffusion Representation Learning
We study why diffusion autoencoders can achieve similar image quality while learning substantially different latent structures. We trace this behaviour to optimisation dynamics; we analyse curves of image reconstruction against latent representation quality, revealing trajectories that organise around two distinct regimes early in training. Models in the reconstruction regime prioritise image fidelity early, whereas those in the disentanglement regime improve reconstruction and disentanglement more gradually. We hypothesise that this behaviour can be influenced by targeting shortcut pathways in the diffusion U-Net and controlling early noise-level exposure, thereby shaping the reconstruction-disentanglement trade-off during training. To steer optimisation toward stronger representations, we introduce SteeringDRL, combining gated residual U-Nets with a simple noise-level exposure curriculum for training. Across disentanglement benchmarks, SteeringDRL improves representation quality and reduces seed sensitivity. Our method further extends to spatial disentanglement in object-centric learning, improving segmentation quality on synthetic and real-world datasets.