用扩散模型和强化学习解决视觉与科学中的逆问题。
Solving Bayesian inverse problems with diffusion priors and off-policy RL
- 基于非策略强化学习训练条件扩散后验模型。
- 在复杂逆问题上优于现有无训练方法。
- 适合需要精准概率推断的科研与图像重建场景。
本文提出了一种实用方法，将近期提出的非策略强化学习目标相对轨迹平衡（RTB）应用于贝叶斯逆问题的求解。通过结合离线回溯探索等技术，利用预训练的无条件先验模型，使用RTB训练条件扩散后验模型，以应对视觉与科学领域中具有挑战性的线性和非线性逆问题。结果表明，现有无训练的扩散后验方法在潜在空间中因固有偏差难以实现有效后验推断。
原文摘要 · Abstract (English)
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically solve Bayesian inverse problems optimally. We extend the original work by using RTB to train conditional diffusion model posteriors from pretrained unconditional priors for challenging linear and non-linear inverse problems in vision, and science. We use the objective alongside techniques such as off-policy backtracking exploration to improve training. Importantly, our results show that existing training-free diffusion posterior methods struggle to perform effective posterior inference in latent space due to inherent biases.
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